├── README.md
├── .gitignore
├── ProjectProposal_Group141_WI24.ipynb
├── oasis_longitudinal.csv
└── DataCheckpoint_Group141_WI24.ipynb
/README.md:
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1 | This is your group repo for your final project for COGS108.
2 |
3 | This repository is private, and is only visible to the course instructors and your group mates; it is not visible to anyone else.
4 |
5 | Template notebooks for each component are provided. Only work on the notebook prior to its due date. After each submission is due, move onto the next notebook (For example, after the proposal is due, start working in the Data Checkpoint notebook).
6 |
7 | This repository will be frozen on the final project due date. No further changes can be made after that time.
8 |
9 | Your project proposal and final project will be graded based solely on the corresponding project notebooks in this repository.
10 |
11 | Template Jupyter notebooks have been included, with your group number replacing the XXX in the following file names. For each due date, make sure you have a notebook present in this repository by each due date with the following name (where XXX is replaced by your group number):
12 |
13 | - `ProjectProposal_groupXXX.ipynb`
14 | - `DataCheckpoint_groupXXX.ipynb`
15 | - `EDACheckpoint_groupXXX.ipynb`
16 | - `FinalProject_groupXXX.ipynb`
17 |
18 | This is *your* repo. You are free to manage the repo as you see fit, edit this README, add data files, add scripts, etc. So long as there are the four files above on due dates with the required information, the rest is up to you all.
19 |
20 | Also, you are free and encouraged to share this project after the course and to add it to your portfolio. Just be sure to fork it to your GitHub at the end of the quarter!
21 |
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/.gitignore:
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1 | # Byte-compiled / optimized / DLL files
2 | __pycache__/
3 | *.py[cod]
4 | *$py.class
5 |
6 | # C extensions
7 | *.so
8 |
9 | # Distribution / packaging
10 | .Python
11 | build/
12 | develop-eggs/
13 | dist/
14 | downloads/
15 | eggs/
16 | .eggs/
17 | lib/
18 | lib64/
19 | parts/
20 | sdist/
21 | var/
22 | wheels/
23 | share/python-wheels/
24 | *.egg-info/
25 | .installed.cfg
26 | *.egg
27 | MANIFEST
28 |
29 | # PyInstaller
30 | # Usually these files are written by a python script from a template
31 | # before PyInstaller builds the exe, so as to inject date/other infos into it.
32 | *.manifest
33 | *.spec
34 |
35 | # Installer logs
36 | pip-log.txt
37 | pip-delete-this-directory.txt
38 |
39 | # Unit test / coverage reports
40 | htmlcov/
41 | .tox/
42 | .nox/
43 | .coverage
44 | .coverage.*
45 | .cache
46 | nosetests.xml
47 | coverage.xml
48 | *.cover
49 | *.py,cover
50 | .hypothesis/
51 | .pytest_cache/
52 | cover/
53 |
54 | # Translations
55 | *.mo
56 | *.pot
57 |
58 | # Django stuff:
59 | *.log
60 | local_settings.py
61 | db.sqlite3
62 | db.sqlite3-journal
63 |
64 | # Flask stuff:
65 | instance/
66 | .webassets-cache
67 |
68 | # Scrapy stuff:
69 | .scrapy
70 |
71 | # Sphinx documentation
72 | docs/_build/
73 |
74 | # PyBuilder
75 | .pybuilder/
76 | target/
77 |
78 | # Jupyter Notebook
79 | .ipynb_checkpoints
80 |
81 | # IPython
82 | profile_default/
83 | ipython_config.py
84 |
85 | # pyenv
86 | # For a library or package, you might want to ignore these files since the code is
87 | # intended to run in multiple environments; otherwise, check them in:
88 | # .python-version
89 |
90 | # pipenv
91 | # According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
92 | # However, in case of collaboration, if having platform-specific dependencies or dependencies
93 | # having no cross-platform support, pipenv may install dependencies that don't work, or not
94 | # install all needed dependencies.
95 | #Pipfile.lock
96 |
97 | # poetry
98 | # Similar to Pipfile.lock, it is generally recommended to include poetry.lock in version control.
99 | # This is especially recommended for binary packages to ensure reproducibility, and is more
100 | # commonly ignored for libraries.
101 | # https://python-poetry.org/docs/basic-usage/#commit-your-poetrylock-file-to-version-control
102 | #poetry.lock
103 |
104 | # pdm
105 | # Similar to Pipfile.lock, it is generally recommended to include pdm.lock in version control.
106 | #pdm.lock
107 | # pdm stores project-wide configurations in .pdm.toml, but it is recommended to not include it
108 | # in version control.
109 | # https://pdm.fming.dev/#use-with-ide
110 | .pdm.toml
111 |
112 | # PEP 582; used by e.g. github.com/David-OConnor/pyflow and github.com/pdm-project/pdm
113 | __pypackages__/
114 |
115 | # Celery stuff
116 | celerybeat-schedule
117 | celerybeat.pid
118 |
119 | # SageMath parsed files
120 | *.sage.py
121 |
122 | # Environments
123 | .env
124 | .venv
125 | env/
126 | venv/
127 | ENV/
128 | env.bak/
129 | venv.bak/
130 |
131 | # Spyder project settings
132 | .spyderproject
133 | .spyproject
134 |
135 | # Rope project settings
136 | .ropeproject
137 |
138 | # mkdocs documentation
139 | /site
140 |
141 | # mypy
142 | .mypy_cache/
143 | .dmypy.json
144 | dmypy.json
145 |
146 | # Pyre type checker
147 | .pyre/
148 |
149 | # pytype static type analyzer
150 | .pytype/
151 |
152 | # Cython debug symbols
153 | cython_debug/
154 |
155 | # PyCharm
156 | # JetBrains specific template is maintained in a separate JetBrains.gitignore that can
157 | # be found at https://github.com/github/gitignore/blob/main/Global/JetBrains.gitignore
158 | # and can be added to the global gitignore or merged into this file. For a more nuclear
159 | # option (not recommended) you can uncomment the following to ignore the entire idea folder.
160 | #.idea/
161 |
162 |
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/ProjectProposal_Group141_WI24.ipynb:
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1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "id": "42a14256-91c8-446e-92a7-ab6bf11055d3",
6 | "metadata": {},
7 | "source": [
8 | "# **COGS 108 - Project Proposal**\n",
9 | "\n",
10 | "# **Names**\n",
11 | "\n",
12 | "- Shivangi Gupta\n",
13 | "\n",
14 | "- Joseph Hwang\n",
15 | "\n",
16 | "- Zijun Yang\n",
17 | "\n",
18 | "- Johnny Gonzales\n",
19 | "\n",
20 | "- Tanishq Rathore\n",
21 | "\n",
22 | "# **Research Question**\n",
23 | "\n",
24 | "Utilizing clinical MRI Data and personal details of an individual, can we predict via machine learning model whether\n",
25 | "an individual will have an onset of Alzheimer's disease? Features the model will be trained on include variables such as Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), gender, age, years of education, socioeconomic status, Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF).\n",
26 | "\n",
27 | "## **Background and Prior Work**\n",
28 | "\n",
29 | "Background\n",
30 | "\n",
31 | "Advancements in healthcare, improvements in living conditions, and\n",
32 | "breakthroughs in medicine have collectively contributed to longer life\n",
33 | "expectancies worldwide; simultaneously, developed countries are also\n",
34 | "experiencing declining fertility\n",
35 | "rates.[1](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
36 | "The combination of these two circumstances has resulted in the\n",
37 | "proportion of older people within populations to steadily increase. The\n",
38 | "World Health Organization (WHO) reported that “in 2020, the number of\n",
39 | "people aged 60 and older outnumbered children younger than 5\n",
40 | "years”.[2](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
41 | "In addition, they also state that “between 2015 and 2050, the proportion\n",
42 | "of the world’s population over 60 years will nearly double from 12% to\n",
43 | "22%”. As a result, it is reasonable that we examine common health\n",
44 | "conditions associated with older age, one being Alzheimer’s disease.\n",
45 | "\n",
46 | "So what is Alzheimer’s disease? Alzheimer’s disease is a progressive\n",
47 | "neurodegenerative brain disorder that impairs memory and cognitive\n",
48 | "functions. It is the most common cause of dementia and affects about 6.5\n",
49 | "million people in the United States who are aged 65 and\n",
50 | "older.[3](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
51 | "At the moment, there are no cures for the disease but medicines may\n",
52 | "improve or slow the progression of\n",
53 | "symptoms.[3](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
54 | "As such, it is our project to create a model that is able to predict\n",
55 | "Alzheimer’s disease based on clinical data that include factors that\n",
56 | "show risk and progression of the disease.\n",
57 | "\n",
58 | "There are several other projects that have asked similar questions and\n",
59 | "approached similar problems for other diseases. For instance, one study\n",
60 | "tried to use machine learning methods to predict risk of cardiovascular\n",
61 | "disease based on major contributing\n",
62 | "factors.[4](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
63 | "Similarly, another paper used machine learning and ranker-based feature\n",
64 | "selection methods to predict eye diseases based on\n",
65 | "symptoms.[5](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
66 | "Lastly, there was a paper that predicted thyroid disease using selective\n",
67 | "features and machine learning\n",
68 | "techniques.[6](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
69 | "All three papers seem to have been relatively successful in predicting\n",
70 | "the disease based on distinct features. Evidently, training machine\n",
71 | "learning models based on datasets which contain factors and indicators\n",
72 | "for a given disease is not a novel format of question and method; we\n",
73 | "hope to achieve similarly for Alzheimer’s disease.\n",
74 | "\n",
75 | "In-Depth Study Analysis\n",
76 | "\n",
77 | "Our group analyzed two studies published in the National Institute of\n",
78 | "Health’s (NIH) journal database. The first study[7](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) employed\n",
79 | "machine learning models to predict early-stage Alzheimer's Disease using\n",
80 | "Open Access Series of Imaging Studies (OASIS) data, focusing on metrics\n",
81 | "like precision, recall, accuracy, and F1-score. The authors, with\n",
82 | "backgrounds in technology and health research, aimed to enhance early\n",
83 | "diagnosis, potentially lowering Alzheimer's mortality rates. The study\n",
84 | "demonstrated that machine learning techniques such as decision trees,\n",
85 | "random forests, SVM, gradient boosting, and voting classifiers can\n",
86 | "effectively predict early-stage Alzheimer's Disease with an accuracy of\n",
87 | "up to 83%. This achievement highlights the critical role of data science\n",
88 | "in identifying Alzheimer's at an early phase, leveraging feature\n",
89 | "selection and advanced algorithms to enhance diagnostic accuracy. Early\n",
90 | "detection is crucial for timely intervention, potentially mitigating the\n",
91 | "disease's progression and impact on patients and their families (Kavitha\n",
92 | "et al.).\n",
93 | "\n",
94 | "The second study[8](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) titled\n",
95 | "“Application of machine learning methods for diagnosis of dementia based\n",
96 | "on the 10/66 battery of cognitive function tests in south India”\n",
97 | "investigated the use of machine learning for diagnosing dementia in\n",
98 | "South India, employing the culturally and educationally fair 10/66\n",
99 | "battery of cognitive function tests designed for use in low and\n",
100 | "middle-income countries. Through the analysis of neuropsychological\n",
101 | "data, demographic information, and normative data, the research applied\n",
102 | "Jrip classification algorithm among others, achieving high diagnostic\n",
103 | "accuracy. This approach demonstrates the potential to streamline the\n",
104 | "diagnostic process, making it quicker and more accessible for clinicians\n",
105 | "and patients in India, thereby addressing the significant healthcare\n",
106 | "challenge of efficiently identifying dementia in community settings\n",
107 | "(Bhagyashree et al).\n",
108 | "\n",
109 | "In-Depth Analysis of Similar Projects\n",
110 | "\n",
111 | "Our group also delved into actual Kaggle projects that are directly\n",
112 | "associated with the dataset we’ve chosen to use, delving into EDA and\n",
113 | "prediction models using Scikit-Learn and Tensorflow. The first\n",
114 | "project[9](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
115 | "I will discuss starts with an introduction to Alzheimer's disease and\n",
116 | "the problem statement of estimating the Clinical Dementia Rating (CDR)\n",
117 | "using MRI dataset features. It progresses through data loading and\n",
118 | "preprocessing, including null value handling and normalization, and\n",
119 | "employs machine learning techniques, specifically mentioning model\n",
120 | "training with hyperparameter tuning for XGBClassifier and\n",
121 | "GradientBoostingClassifier. The notebook concludes with predictions and\n",
122 | "performance evaluation, indicated by confusion matrix and classification\n",
123 | "report visualizations, and the model was able to reach a final accuracy\n",
124 | "of \\~80%.\n",
125 | "\n",
126 | "The second project[10](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) tried to predict the Clinical Rating of Alzheimer's disease (CRA) by\n",
127 | "integrating data loading, visualization, and extensive machine learning,\n",
128 | "including the use of TensorFlow for neural network models. It explored\n",
129 | "various machine learning models, with a special emphasis on model\n",
130 | "training and evaluation, culminating in the finding that the\n",
131 | "DecisionTreeClassifier performed the best among the models tested. The\n",
132 | "conclusion stressed the need for more data to enhance the precision of\n",
133 | "Alzheimer's disease predictions, highlighting the challenge of data\n",
134 | "scarcity in achieving accurate diagnostic models.\n",
135 | "\n",
136 | "**References**\n",
137 | "\n",
138 | "1. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/) Nargund G. (2009) Declining birth rate in Developed Countries: A\n",
139 | " radical policy re-think is required. *Facts, views & vision in\n",
140 | " ObGyn, 1(3), 191–193.*\n",
141 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
142 | "\n",
143 | "2. [^](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) World Health Organization. (1 Oct 2022) Ageing and health. *World\n",
144 | " Health Organization*.\n",
145 | " [https://www.who.int/news-room/fact-sheets/detail/ageing-and-health](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
146 | "\n",
147 | "3. [^](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447) Mayo Foundation for Medical Education and Research. (30\n",
148 | " August 2023) Alzheimer’s disease. *Mayo Clinic*.\n",
149 | " [https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
150 | "\n",
151 | "4. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/) Peng, M., Hou, F., Cheng, Z., Shen, T., Liu, K., Zhao, C., &\n",
152 | " Zheng, W. (23 Mar 2023) Prediction of cardiovascular disease risk\n",
153 | " based on major contributing features. *Scientific reports, 13(1),\n",
154 | " 4778*.\n",
155 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
156 | "\n",
157 | "5. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/) Marouf, A. A., Mottalib, M. M., Alhajj, R., Rokne, J., &\n",
158 | " Jafarullah, O. (24 Dec 2022) An Efficient Approach to Predict Eye\n",
159 | " Diseases from Symptoms Using Machine Learning and Ranker-Based\n",
160 | " Feature Selection Methods. *Bioengineering (Basel, Switzerland),\n",
161 | " 10(1), 25*.\n",
162 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
163 | "\n",
164 | "6. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/) Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V.,\n",
165 | " Rodríguez, C. L., & Ashraf, I. (13 Aug 2022). Thyroid Disease\n",
166 | " Prediction Using Selective Features and Machine Learning\n",
167 | " Techniques. *Cancers, 14(16), 3914*.\n",
168 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
169 | "\n",
170 | "7. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) Kavitha C, Mani V, Srividhya SR, Khalaf OI, Tavera Romero CA.\n",
171 | " Early-Stage Alzheimer's Disease Prediction Using Machine Learning\n",
172 | " Models. Front Public Health. 2022 Mar 3;10:853294. doi:\n",
173 | " 10.3389/fpubh.2022.853294. PMID: 35309200; PMCID: PMC8927715.\n",
174 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/)\n",
175 | "\n",
176 | "8. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) Bhagyashree SIR, Nagaraj K, Prince M, Fall CHD, Krishna M.\n",
177 | " Diagnosis of Dementia by Machine learning methods in\n",
178 | " Epidemiological studies: a pilot exploratory study from south\n",
179 | " India. Soc Psychiatry Psychiatr Epidemiol. 2018 Jan;53(1):77-86.\n",
180 | " doi: 10.1007/s00127-017-1410-0. Epub 2017 Jul 11. PMID: 28698926;\n",
181 | " PMCID: PMC6138240.\n",
182 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/)\n",
183 | "\n",
184 | "9. [^](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri) Reddy, Shreyas. 2021. Alzheimer's analysis using MRI, February\n",
185 | " 8, 2024.\n",
186 | " [https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
187 | "\n",
188 | "10. [^](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) Andrew. 2017. Predict alzheimer disease sl and tf, February 8, 2024.\n",
189 | " [https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf)\n",
190 | "\n",
191 | "# **Hypothesis**\n",
192 | "\n",
193 | "Our project's hypothesis is the following: \"It is possible to predict the onset of Alzheimers based on the combination of (1) clinical data* and (2) personal features such as gender, age, years of education, and socioeconomic status.\" We believe that we will be able to successfully train a model that is able to predict the onset of Alzheimer's disease because, as mentioned in the background portion of the proposal, there has been numerous successful machine learning models trained on clinical data to predict the onset of a disease. The clinical data provide variables that capture both the cognitive and structural changes associated with the disease's progression. Incorporating personal features such as gender and age in the prediction model is justified by extensive research indicating that these factors can influence the risk and progression rate of Alzheimer's disease. Together, we believe that this will be enough data to train a model to predict the onset of Alzheimer's disease.\n",
194 | "\n",
195 | "*Clinical data include Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and the Atlas Scaling Factor (ASF)\n",
196 | "\n",
197 | "# **Data**\n",
198 | "\n",
199 | "1. The ideal dataset to train our machine learning model would contain clinical and personal variables that capture the progression and risk level of Alzheimer's disease. These datasets would have to be found online from reputable medical research centers or through large-scale longitudinal studies focusing on aging and dementia. Within these studies, MRI scans and protocols should be conducted using standardized procedures for consistency across the dataset.\n",
200 | "\n",
201 | " In addition, a large sample size would be ideal, although preliminary searches in clinical data seem to range around 500 for diseases. We also note that although incorporating many variables will make it difficult to interpret and determine what the main driving factor is in which makes the model predict the onset of the disease in an individual, our goal is just to create a model that is able to predict the onset given a set of clinical and personal variables that are commonly available. Perhaps determining which factor influences the model's decision the most may be another interesting avenue to explore.\n",
202 | "\n",
203 | " The clinical variables in which we want to examine are the following:\n",
204 | " - Mini Mental State Examination (MMSE): A measure of cognitive impairment.\n",
205 | " - Clinical Dementia Rating (CDR): A numerical scale used to quantify the severity of symptoms of dementia.\n",
206 | " - Estimated Total Intracranial Volume (eTIV)\n",
207 | " - Normalize Whole Brain Volume (nWBV) \n",
208 | " - Atlas Scaling Factor (ASF): Measures of brain volume and structure, obtained from MRI scans, that can indicate brain atrophy associated with Alzheimer's.\n",
209 | " - Visit Number\n",
210 | "\n",
211 | " Personal features that are commonly associated with Alzhiemer's risk that we also want to include in our project are the following:\n",
212 | " - Gender\n",
213 | " - Age\n",
214 | " - Years of Education\n",
215 | " - Socioeconomic Status\n",
216 | " - Indicators of cognitive reserve\n",
217 | "\n",
218 | "2. Searching for potential datasets, we found that there is the Open Access Series of Imaging Studies (OASIS) which makes available MRI data sets related to Alzheimer's disease. We were able to download the dataset and found that it included clinical and personal variables that we ideally wanted to incorporate. OASIS makes available these data to be accessible through csv files which we can download and utilize independently. There is also the UK Biobank which is a large scale database with 500,000 participants which include patient information and brain imaging data, but does not have all indicators. Lastly, we found data from the National Alzheimer's Coordinating Center (NACC) which collects and stores data from Alzheimer's research centers around the US. The data appears reliable but is not in a standardized format (which requires cleaning as a result).\n",
219 | "\n",
220 | "# **Ethics & Privacy**\n",
221 | "\n",
222 | "Ethics & Privacy Considerations:\n",
223 | "\n",
224 | "Biases/Privacy/Terms of Use Issues with Proposed Data:\n",
225 | "\n",
226 | "1. The potential datasets considered, such as OASIS MRI, UK Biobank,\n",
227 | " and NACC, may have biases and privacy considerations. For\n",
228 | " instance, the OASIS MRI project's terms of use and participant\n",
229 | " selection could introduce biases. UK Biobank, despite its size,\n",
230 | " might not include a diverse representation of certain populations,\n",
231 | " leading to potential biases in the dataset.\n",
232 | "\n",
233 | "Potential Biases in Dataset Composition and Collection:\n",
234 | "\n",
235 | "2. Biases may arise in dataset composition and collection, affecting\n",
236 | " the equitable analysis of Alzheimer's prediction. For example, if\n",
237 | " the data predominantly includes participants from specific\n",
238 | " demographic groups, it could introduce biases in the model.\n",
239 | " Additionally, variations in data collection methods across\n",
240 | " different research centers, as in the case of NACC, may impact\n",
241 | " standardization, potentially leading to biases.\n",
242 | "\n",
243 | "Detection and Mitigation of Biases:\n",
244 | "\n",
245 | "3. To detect biases, the group will conduct a thorough review of the\n",
246 | " dataset sources, including participant demographics and\n",
247 | " recruitment methods. During data preprocessing, the team will\n",
248 | " analyze variables for potential biases, ensuring a balanced\n",
249 | " representation. The group plans to collaborate with experts in the\n",
250 | " field and seek external input to validate the fairness and\n",
251 | " inclusivity of the dataset.\n",
252 | "\n",
253 | "Other Issues Related to Privacy and Equitable Impact:\n",
254 | "\n",
255 | "4. Privacy concerns arise from the sensitive nature of medical data,\n",
256 | " especially in Alzheimer's research. Ensuring participant anonymity\n",
257 | " and adhering to privacy regulations are paramount. Equitable\n",
258 | " impact considerations involve understanding if the model's\n",
259 | " predictions could disproportionately affect certain groups. It is\n",
260 | " essential to communicate findings responsibly, avoiding\n",
261 | " reinforcing existing biases or stigmatizing specific populations.\n",
262 | "\n",
263 | "Handling Identified Issues:\n",
264 | "\n",
265 | "5. The group commits to transparently communicating any identified\n",
266 | " biases throughout the research process. Mitigation strategies will\n",
267 | " be implemented during data preprocessing and model development.\n",
268 | " The team will consider alternative datasets or additional sampling\n",
269 | " methods if biases persist. Ethical review boards will be\n",
270 | " consulted, and the group aims to publish findings with a clear\n",
271 | " acknowledgment of potential limitations and biases, promoting\n",
272 | " responsible and equitable use of the predictive model.\n",
273 | "\n",
274 | "In summary, the group is dedicated to addressing ethical concerns\n",
275 | "comprehensively, from data collection to analysis and post-analysis.\n",
276 | "Transparency, collaboration with experts, and continuous evaluation of\n",
277 | "potential biases will guide the research, ensuring responsible and\n",
278 | "ethical development of the Alzheimer's prediction model.\n",
279 | "\n",
280 | "# **Team Expectations**\n",
281 | "\n",
282 | "1. Clear communication and relatively reasonable responsiveness to messages sent in our Discord group chat.\n",
283 | "2. Shared Responsibility and Accountability: finish all tasked work by the designated due date.\n",
284 | "3. Maintain quality work and attention to detail; ask questions when necessary.\n",
285 | "4. Attendance and Participation: during designated days in which we meet (either through Zoom or in-person), everyone should be present unless notified previously in advance. \n",
286 | "\n",
287 | "\n",
288 | "# **Project Timeline Proposal**\n",
289 | "\n",
290 | "| **Meeting Date** | **Meeting Time** | **Completed Before Meeting** | **Discuss at Meeting** |\n",
291 | "|-----------|-----------|-----------------------------|-----------------------|\n",
292 | "| 2/10 | 1 PM | Edit, finalize, and submit proposal; Search for datasets (everyone) | Discuss Wrangling and possible analytical approaches; Assign group members to lead each specific part |\n",
293 | "| 2/16 | 6 PM | Import & Wrangle Data (Johnny); EDA (Shivangi) | Review/Edit wrangling/EDA; Discuss Analysis Plan |\n",
294 | "| 2/23 | 12 PM | Finalize wrangling/EDA; Begin Analysis (Joseph; Zijun) | Discuss/edit Analysis; Complete project check-in |\n",
295 | "| 3/16 | 12 PM | Complete analysis; Draft results/conclusion/discussion (Tanishq); everyone will make/record our presentation | Discuss/edit full project; Record our video |\n",
296 | "| 3/20 | Before 11:59 PM | Finalize/proofread project conclusion and submit report and video (everyone) | Turn in Final Project & Group Project Surveys |"
297 | ]
298 | },
299 | {
300 | "cell_type": "markdown",
301 | "id": "ba0dd509",
302 | "metadata": {},
303 | "source": []
304 | }
305 | ],
306 | "metadata": {
307 | "language_info": {
308 | "name": "python"
309 | }
310 | },
311 | "nbformat": 4,
312 | "nbformat_minor": 5
313 | }
314 |
--------------------------------------------------------------------------------
/oasis_longitudinal.csv:
--------------------------------------------------------------------------------
1 | Subject ID,MRI ID,Group,Visit,MR Delay,M/F,Hand,Age,EDUC,SES,MMSE,CDR,eTIV,nWBV,ASF
2 | OAS2_0001,OAS2_0001_MR1,Nondemented,1,0,M,R,87,14,2,27,0,1987,0.696,0.883
3 | OAS2_0001,OAS2_0001_MR2,Nondemented,2,457,M,R,88,14,2,30,0,2004,0.681,0.876
4 | OAS2_0002,OAS2_0002_MR1,Demented,1,0,M,R,75,12,,23,0.5,1678,0.736,1.046
5 | OAS2_0002,OAS2_0002_MR2,Demented,2,560,M,R,76,12,,28,0.5,1738,0.713,1.010
6 | OAS2_0002,OAS2_0002_MR3,Demented,3,1895,M,R,80,12,,22,0.5,1698,0.701,1.034
7 | OAS2_0004,OAS2_0004_MR1,Nondemented,1,0,F,R,88,18,3,28,0,1215,0.710,1.444
8 | OAS2_0004,OAS2_0004_MR2,Nondemented,2,538,F,R,90,18,3,27,0,1200,0.718,1.462
9 | OAS2_0005,OAS2_0005_MR1,Nondemented,1,0,M,R,80,12,4,28,0,1689,0.712,1.039
10 | OAS2_0005,OAS2_0005_MR2,Nondemented,2,1010,M,R,83,12,4,29,0.5,1701,0.711,1.032
11 | OAS2_0005,OAS2_0005_MR3,Nondemented,3,1603,M,R,85,12,4,30,0,1699,0.705,1.033
12 | OAS2_0007,OAS2_0007_MR1,Demented,1,0,M,R,71,16,,28,0.5,1357,0.748,1.293
13 | OAS2_0007,OAS2_0007_MR3,Demented,3,518,M,R,73,16,,27,1,1365,0.727,1.286
14 | OAS2_0007,OAS2_0007_MR4,Demented,4,1281,M,R,75,16,,27,1,1372,0.710,1.279
15 | OAS2_0008,OAS2_0008_MR1,Nondemented,1,0,F,R,93,14,2,30,0,1272,0.698,1.380
16 | OAS2_0008,OAS2_0008_MR2,Nondemented,2,742,F,R,95,14,2,29,0,1257,0.703,1.396
17 | OAS2_0009,OAS2_0009_MR1,Demented,1,0,M,R,68,12,2,27,0.5,1457,0.806,1.205
18 | OAS2_0009,OAS2_0009_MR2,Demented,2,576,M,R,69,12,2,24,0.5,1480,0.791,1.186
19 | OAS2_0010,OAS2_0010_MR1,Demented,1,0,F,R,66,12,3,30,0.5,1447,0.769,1.213
20 | OAS2_0010,OAS2_0010_MR2,Demented,2,854,F,R,68,12,3,29,0.5,1482,0.752,1.184
21 | OAS2_0012,OAS2_0012_MR1,Nondemented,1,0,F,R,78,16,2,29,0,1333,0.748,1.316
22 | OAS2_0012,OAS2_0012_MR2,Nondemented,2,730,F,R,80,16,2,29,0,1323,0.738,1.326
23 | OAS2_0012,OAS2_0012_MR3,Nondemented,3,1598,F,R,83,16,2,29,0,1323,0.718,1.327
24 | OAS2_0013,OAS2_0013_MR1,Nondemented,1,0,F,R,81,12,4,30,0,1230,0.715,1.427
25 | OAS2_0013,OAS2_0013_MR2,Nondemented,2,643,F,R,82,12,4,30,0,1212,0.720,1.448
26 | OAS2_0013,OAS2_0013_MR3,Nondemented,3,1456,F,R,85,12,4,29,0,1225,0.710,1.433
27 | OAS2_0014,OAS2_0014_MR1,Demented,1,0,M,R,76,16,3,21,0.5,1602,0.697,1.096
28 | OAS2_0014,OAS2_0014_MR2,Demented,2,504,M,R,77,16,3,16,1,1590,0.696,1.104
29 | OAS2_0016,OAS2_0016_MR1,Demented,1,0,M,R,88,8,4,25,0.5,1651,0.660,1.063
30 | OAS2_0016,OAS2_0016_MR2,Demented,2,707,M,R,90,8,4,23,0.5,1668,0.646,1.052
31 | OAS2_0017,OAS2_0017_MR1,Nondemented,1,0,M,R,80,12,3,29,0,1783,0.752,0.985
32 | OAS2_0017,OAS2_0017_MR3,Nondemented,3,617,M,R,81,12,3,27,0.5,1814,0.759,0.968
33 | OAS2_0017,OAS2_0017_MR4,Nondemented,4,1861,M,R,85,12,3,30,0,1820,0.755,0.964
34 | OAS2_0017,OAS2_0017_MR5,Nondemented,5,2400,M,R,86,12,3,27,0,1813,0.761,0.968
35 | OAS2_0018,OAS2_0018_MR1,Converted,1,0,F,R,87,14,1,30,0,1406,0.715,1.248
36 | OAS2_0018,OAS2_0018_MR3,Converted,3,489,F,R,88,14,1,29,0,1398,0.713,1.255
37 | OAS2_0018,OAS2_0018_MR4,Converted,4,1933,F,R,92,14,1,27,0.5,1423,0.696,1.234
38 | OAS2_0020,OAS2_0020_MR1,Converted,1,0,M,R,80,20,1,29,0,1587,0.693,1.106
39 | OAS2_0020,OAS2_0020_MR2,Converted,2,756,M,R,82,20,1,28,0.5,1606,0.677,1.093
40 | OAS2_0020,OAS2_0020_MR3,Converted,3,1563,M,R,84,20,1,26,0.5,1597,0.666,1.099
41 | OAS2_0021,OAS2_0021_MR1,Demented,1,0,M,R,72,20,1,26,0.5,1911,0.719,0.919
42 | OAS2_0021,OAS2_0021_MR2,Demented,2,1164,M,R,76,20,1,25,0.5,1926,0.736,0.911
43 | OAS2_0022,OAS2_0022_MR1,Nondemented,1,0,F,R,61,16,3,30,0,1313,0.805,1.337
44 | OAS2_0022,OAS2_0022_MR2,Nondemented,2,828,F,R,64,16,3,29,0,1316,0.796,1.333
45 | OAS2_0023,OAS2_0023_MR1,Demented,1,0,F,R,86,12,4,21,0.5,1247,0.662,1.407
46 | OAS2_0023,OAS2_0023_MR2,Demented,2,578,F,R,87,12,4,21,0.5,1250,0.652,1.405
47 | OAS2_0026,OAS2_0026_MR1,Demented,1,0,M,R,82,12,3,27,0.5,1420,0.713,1.236
48 | OAS2_0026,OAS2_0026_MR2,Demented,2,673,M,R,84,12,3,27,0.5,1445,0.695,1.214
49 | OAS2_0027,OAS2_0027_MR1,Nondemented,1,0,F,R,69,12,3,29,0,1365,0.783,1.286
50 | OAS2_0027,OAS2_0027_MR2,Nondemented,2,609,F,R,71,12,3,30,0,1360,0.782,1.291
51 | OAS2_0027,OAS2_0027_MR3,Nondemented,3,1234,F,R,73,12,3,30,0,1358,0.775,1.293
52 | OAS2_0027,OAS2_0027_MR4,Nondemented,4,1779,F,R,74,12,3,30,0,1353,0.772,1.297
53 | OAS2_0028,OAS2_0028_MR1,Demented,1,0,M,R,64,18,2,22,0.5,1547,0.737,1.134
54 | OAS2_0028,OAS2_0028_MR2,Demented,2,610,M,R,66,18,2,21,1,1562,0.717,1.124
55 | OAS2_0029,OAS2_0029_MR1,Nondemented,1,0,F,R,77,12,4,29,0,1377,0.734,1.275
56 | OAS2_0029,OAS2_0029_MR2,Nondemented,2,1099,F,R,80,12,4,30,0,1390,0.735,1.263
57 | OAS2_0030,OAS2_0030_MR1,Nondemented,1,0,F,R,60,18,1,30,0,1402,0.822,1.252
58 | OAS2_0030,OAS2_0030_MR2,Nondemented,2,932,F,R,62,18,1,30,0,1392,0.817,1.261
59 | OAS2_0031,OAS2_0031_MR1,Converted,1,0,F,R,86,12,3,30,0,1430,0.718,1.227
60 | OAS2_0031,OAS2_0031_MR2,Converted,2,446,F,R,88,12,3,30,0,1445,0.719,1.215
61 | OAS2_0031,OAS2_0031_MR3,Converted,3,1588,F,R,91,12,3,28,0.5,1463,0.696,1.199
62 | OAS2_0032,OAS2_0032_MR1,Demented,1,0,M,R,90,12,3,21,0.5,1307,0.679,1.342
63 | OAS2_0032,OAS2_0032_MR2,Demented,2,642,M,R,92,12,3,24,0.5,1311,0.676,1.339
64 | OAS2_0034,OAS2_0034_MR1,Nondemented,1,0,F,R,79,16,1,29,0,1466,0.703,1.197
65 | OAS2_0034,OAS2_0034_MR2,Nondemented,2,489,F,R,80,16,1,30,0,1450,0.698,1.210
66 | OAS2_0034,OAS2_0034_MR3,Nondemented,3,1287,F,R,82,16,1,30,0,1460,0.695,1.202
67 | OAS2_0034,OAS2_0034_MR4,Nondemented,4,1884,F,R,84,16,1,30,0,1453,0.684,1.208
68 | OAS2_0035,OAS2_0035_MR1,Nondemented,1,0,F,R,88,12,4,30,0,1336,0.738,1.313
69 | OAS2_0035,OAS2_0035_MR2,Nondemented,2,405,F,R,89,12,4,27,0,1329,0.733,1.320
70 | OAS2_0036,OAS2_0036_MR1,Nondemented,1,0,F,R,69,13,4,30,0,1359,0.789,1.291
71 | OAS2_0036,OAS2_0036_MR3,Nondemented,3,713,F,R,70,13,4,30,0,1361,0.783,1.290
72 | OAS2_0036,OAS2_0036_MR4,Nondemented,4,1770,F,R,73,13,4,30,0,1360,0.773,1.291
73 | OAS2_0036,OAS2_0036_MR5,Nondemented,5,2369,F,R,75,13,4,29,0,1349,0.778,1.301
74 | OAS2_0037,OAS2_0037_MR1,Demented,1,0,M,R,82,12,4,27,0.5,1477,0.729,1.188
75 | OAS2_0037,OAS2_0037_MR2,Demented,2,1123,M,R,85,12,4,29,0.5,1487,0.717,1.180
76 | OAS2_0037,OAS2_0037_MR3,Demented,3,2029,M,R,88,12,4,26,0.5,1483,0.709,1.184
77 | OAS2_0037,OAS2_0037_MR4,Demented,4,2508,M,R,89,12,4,26,0.5,1485,0.706,1.181
78 | OAS2_0039,OAS2_0039_MR1,Demented,1,0,F,R,81,18,2,26,0.5,1174,0.742,1.495
79 | OAS2_0039,OAS2_0039_MR2,Demented,2,486,F,R,83,18,2,25,0.5,1179,0.733,1.488
80 | OAS2_0040,OAS2_0040_MR1,Demented,1,0,M,R,84,6,4,25,0.5,1310,0.727,1.339
81 | OAS2_0040,OAS2_0040_MR2,Demented,2,567,M,R,86,6,4,27,0.5,1320,0.724,1.329
82 | OAS2_0040,OAS2_0040_MR3,Demented,3,1204,M,R,88,6,4,23,0.5,1348,0.713,1.302
83 | OAS2_0041,OAS2_0041_MR1,Converted,1,0,F,R,71,16,1,27,0,1289,0.771,1.362
84 | OAS2_0041,OAS2_0041_MR2,Converted,2,756,F,R,73,16,1,28,0,1295,0.768,1.356
85 | OAS2_0041,OAS2_0041_MR3,Converted,3,1331,F,R,75,16,1,28,0.5,1314,0.760,1.335
86 | OAS2_0042,OAS2_0042_MR1,Nondemented,1,0,F,R,70,17,3,29,0,1640,0.766,1.070
87 | OAS2_0042,OAS2_0042_MR2,Nondemented,2,1008,F,R,73,17,3,29,0,1665,0.748,1.054
88 | OAS2_0043,OAS2_0043_MR1,Demented,1,0,F,R,72,12,4,26,0.5,1453,0.777,1.208
89 | OAS2_0043,OAS2_0043_MR2,Demented,2,491,F,R,73,12,4,26,0.5,1451,0.757,1.210
90 | OAS2_0044,OAS2_0044_MR1,Demented,1,0,M,R,68,14,4,21,1,1333,0.685,1.317
91 | OAS2_0044,OAS2_0044_MR2,Demented,2,352,M,R,69,14,4,15,1,1331,0.678,1.318
92 | OAS2_0044,OAS2_0044_MR3,Demented,3,866,M,R,71,14,4,22,1,1332,0.679,1.317
93 | OAS2_0045,OAS2_0045_MR1,Nondemented,1,0,F,R,75,18,1,30,0,1317,0.737,1.332
94 | OAS2_0045,OAS2_0045_MR2,Nondemented,2,689,F,R,77,18,1,29,0,1322,0.731,1.327
95 | OAS2_0046,OAS2_0046_MR1,Demented,1,0,F,R,83,15,2,20,0.5,1476,0.750,1.189
96 | OAS2_0046,OAS2_0046_MR2,Demented,2,575,F,R,85,15,2,22,1,1483,0.748,1.183
97 | OAS2_0047,OAS2_0047_MR1,Nondemented,1,0,F,R,77,16,2,29,0,1433,0.723,1.225
98 | OAS2_0047,OAS2_0047_MR2,Nondemented,2,486,F,R,78,16,2,27,0,1414,0.727,1.242
99 | OAS2_0048,OAS2_0048_MR1,Demented,1,0,M,R,66,16,1,19,1,1695,0.711,1.036
100 | OAS2_0048,OAS2_0048_MR2,Demented,2,248,M,R,66,16,1,21,1,1708,0.703,1.028
101 | OAS2_0048,OAS2_0048_MR3,Demented,3,647,M,R,68,16,1,19,1,1712,0.691,1.025
102 | OAS2_0048,OAS2_0048_MR4,Demented,4,970,M,R,68,16,1,7,1,1714,0.682,1.024
103 | OAS2_0048,OAS2_0048_MR5,Demented,5,1233,M,R,69,16,1,4,1,1701,0.676,1.032
104 | OAS2_0049,OAS2_0049_MR1,Nondemented,1,0,F,R,69,16,3,30,0,1491,0.794,1.177
105 | OAS2_0049,OAS2_0049_MR2,Nondemented,2,395,F,R,70,16,3,30,0,1505,0.791,1.166
106 | OAS2_0049,OAS2_0049_MR3,Nondemented,3,687,F,R,71,16,3,30,0,1503,0.788,1.168
107 | OAS2_0050,OAS2_0050_MR1,Demented,1,0,M,R,71,12,4,20,0.5,1461,0.724,1.202
108 | OAS2_0050,OAS2_0050_MR2,Demented,2,538,M,R,72,12,4,17,1,1483,0.695,1.184
109 | OAS2_0051,OAS2_0051_MR1,Nondemented,1,0,F,R,92,23,1,29,0,1454,0.701,1.207
110 | OAS2_0051,OAS2_0051_MR2,Nondemented,2,457,F,R,94,23,1,29,0,1474,0.696,1.190
111 | OAS2_0051,OAS2_0051_MR3,Nondemented,3,1526,F,R,97,23,1,30,0,1483,0.689,1.184
112 | OAS2_0052,OAS2_0052_MR1,Nondemented,1,0,M,R,74,18,2,29,0,1463,0.737,1.199
113 | OAS2_0052,OAS2_0052_MR2,Nondemented,2,1510,M,R,78,18,2,30,0,1484,0.703,1.183
114 | OAS2_0053,OAS2_0053_MR1,Nondemented,1,0,F,R,82,16,3,29,0,1484,0.760,1.183
115 | OAS2_0053,OAS2_0053_MR2,Nondemented,2,842,F,R,84,16,3,28,0,1500,0.744,1.170
116 | OAS2_0054,OAS2_0054_MR1,Converted,1,0,F,R,85,18,1,29,0,1264,0.701,1.388
117 | OAS2_0054,OAS2_0054_MR2,Converted,2,846,F,R,87,18,1,24,0.5,1275,0.683,1.376
118 | OAS2_0055,OAS2_0055_MR1,Nondemented,1,0,M,R,65,13,3,29,0,1362,0.837,1.289
119 | OAS2_0055,OAS2_0055_MR2,Nondemented,2,726,M,R,67,13,3,27,0,1365,0.827,1.285
120 | OAS2_0056,OAS2_0056_MR1,Nondemented,1,0,F,R,71,14,2,28,0,1461,0.756,1.202
121 | OAS2_0056,OAS2_0056_MR2,Nondemented,2,622,F,R,73,14,2,30,0,1456,0.739,1.205
122 | OAS2_0057,OAS2_0057_MR1,Nondemented,1,0,F,R,81,12,2,30,0,1599,0.755,1.098
123 | OAS2_0057,OAS2_0057_MR2,Nondemented,2,640,F,R,83,12,2,29,0,1569,0.757,1.118
124 | OAS2_0057,OAS2_0057_MR3,Nondemented,3,1340,F,R,85,12,2,30,0,1580,0.739,1.111
125 | OAS2_0058,OAS2_0058_MR1,Demented,1,0,M,R,78,14,3,30,0.5,1315,0.707,1.335
126 | OAS2_0058,OAS2_0058_MR2,Demented,2,212,M,R,79,14,3,26,0.5,1308,0.706,1.341
127 | OAS2_0058,OAS2_0058_MR3,Demented,3,764,M,R,80,14,3,29,0.5,1324,0.695,1.326
128 | OAS2_0060,OAS2_0060_MR1,Demented,1,0,M,R,75,13,4,29,0.5,1416,0.766,1.239
129 | OAS2_0060,OAS2_0060_MR2,Demented,2,1290,M,R,78,13,4,28,0.5,1408,0.757,1.247
130 | OAS2_0061,OAS2_0061_MR1,Nondemented,1,0,M,R,68,18,1,30,0,1654,0.747,1.061
131 | OAS2_0061,OAS2_0061_MR2,Nondemented,2,873,M,R,70,18,1,30,0,1660,0.738,1.057
132 | OAS2_0061,OAS2_0061_MR3,Nondemented,3,1651,M,R,72,18,1,30,0,1681,0.729,1.044
133 | OAS2_0062,OAS2_0062_MR1,Nondemented,1,0,F,R,79,18,2,29,0,1641,0.695,1.069
134 | OAS2_0062,OAS2_0062_MR2,Nondemented,2,723,F,R,81,18,2,30,0,1664,0.677,1.055
135 | OAS2_0062,OAS2_0062_MR3,Nondemented,3,1351,F,R,83,18,2,29,0,1667,0.688,1.053
136 | OAS2_0063,OAS2_0063_MR1,Demented,1,0,F,R,80,12,,30,0.5,1430,0.737,1.228
137 | OAS2_0063,OAS2_0063_MR2,Demented,2,490,F,R,81,12,,27,0.5,1453,0.721,1.208
138 | OAS2_0064,OAS2_0064_MR1,Demented,1,0,F,R,78,8,5,23,1,1462,0.691,1.200
139 | OAS2_0064,OAS2_0064_MR2,Demented,2,830,F,R,81,8,5,26,0.5,1459,0.694,1.203
140 | OAS2_0064,OAS2_0064_MR3,Demented,3,1282,F,R,82,8,5,18,0.5,1464,0.682,1.199
141 | OAS2_0066,OAS2_0066_MR1,Demented,1,0,M,R,61,18,1,30,1,1957,0.734,0.897
142 | OAS2_0066,OAS2_0066_MR2,Demented,2,497,M,R,62,18,1,30,0.5,1928,0.731,0.910
143 | OAS2_0067,OAS2_0067_MR1,Nondemented,1,0,M,R,67,12,4,30,0,1440,0.727,1.219
144 | OAS2_0067,OAS2_0067_MR2,Nondemented,2,451,M,R,68,12,4,29,0,1438,0.738,1.220
145 | OAS2_0067,OAS2_0067_MR3,Nondemented,3,1438,M,R,71,12,4,29,0,1455,0.724,1.206
146 | OAS2_0067,OAS2_0067_MR4,Nondemented,4,2163,M,R,73,12,4,28,0,1444,0.722,1.215
147 | OAS2_0068,OAS2_0068_MR1,Nondemented,1,0,F,R,88,12,3,30,0,1428,0.700,1.229
148 | OAS2_0068,OAS2_0068_MR2,Nondemented,2,743,F,R,90,12,3,29,0,1475,0.676,1.190
149 | OAS2_0069,OAS2_0069_MR1,Nondemented,1,0,F,R,81,18,2,29,0,1470,0.687,1.194
150 | OAS2_0069,OAS2_0069_MR2,Nondemented,2,432,F,R,82,18,2,30,0,1471,0.690,1.193
151 | OAS2_0070,OAS2_0070_MR1,Nondemented,1,0,M,R,80,17,1,28,0,1660,0.728,1.057
152 | OAS2_0070,OAS2_0070_MR2,Nondemented,2,672,M,R,82,17,1,29,0,1692,0.723,1.037
153 | OAS2_0070,OAS2_0070_MR3,Nondemented,3,1415,M,R,84,17,1,29,0,1707,0.717,1.028
154 | OAS2_0070,OAS2_0070_MR4,Nondemented,4,1870,M,R,85,17,1,30,0,1724,0.704,1.018
155 | OAS2_0070,OAS2_0070_MR5,Nondemented,5,2386,M,R,86,17,1,30,0,1720,0.705,1.020
156 | OAS2_0071,OAS2_0071_MR1,Demented,1,0,F,R,83,13,2,27,1,1391,0.705,1.262
157 | OAS2_0071,OAS2_0071_MR2,Demented,2,365,F,R,84,13,2,28,1,1402,0.695,1.252
158 | OAS2_0073,OAS2_0073_MR1,Nondemented,1,0,F,R,70,14,3,29,0,1524,0.787,1.151
159 | OAS2_0073,OAS2_0073_MR2,Nondemented,2,580,F,R,72,14,3,28,0,1512,0.777,1.161
160 | OAS2_0073,OAS2_0073_MR3,Nondemented,3,1705,F,R,75,14,3,28,0,1507,0.782,1.164
161 | OAS2_0073,OAS2_0073_MR4,Nondemented,4,2288,F,R,76,14,3,29,0,1490,0.774,1.178
162 | OAS2_0073,OAS2_0073_MR5,Nondemented,5,2517,F,R,77,14,3,29,0,1504,0.769,1.167
163 | OAS2_0075,OAS2_0075_MR1,Demented,1,0,F,R,73,8,5,25,0.5,1151,0.743,1.525
164 | OAS2_0075,OAS2_0075_MR2,Demented,2,567,F,R,75,8,5,22,0.5,1143,0.741,1.535
165 | OAS2_0076,OAS2_0076_MR1,Nondemented,1,0,F,R,66,18,2,30,0,1504,0.725,1.167
166 | OAS2_0076,OAS2_0076_MR2,Nondemented,2,956,F,R,69,18,2,29,0,1536,0.719,1.143
167 | OAS2_0076,OAS2_0076_MR3,Nondemented,3,1663,F,R,71,18,2,30,0,1520,0.718,1.155
168 | OAS2_0077,OAS2_0077_MR1,Nondemented,1,0,M,R,69,16,2,28,0,1848,0.737,0.950
169 | OAS2_0077,OAS2_0077_MR2,Nondemented,2,1393,M,R,73,16,2,29,0,1931,0.722,0.909
170 | OAS2_0078,OAS2_0078_MR1,Nondemented,1,0,M,R,89,16,1,28,0,1631,0.674,1.076
171 | OAS2_0078,OAS2_0078_MR2,Nondemented,2,441,M,R,91,16,1,28,0,1640,0.670,1.070
172 | OAS2_0078,OAS2_0078_MR3,Nondemented,3,1019,M,R,92,16,1,30,0,1662,0.682,1.056
173 | OAS2_0079,OAS2_0079_MR1,Demented,1,0,F,R,69,12,4,23,0.5,1447,0.759,1.213
174 | OAS2_0079,OAS2_0079_MR2,Demented,2,584,F,R,71,12,4,16,1,1492,0.725,1.176
175 | OAS2_0079,OAS2_0079_MR3,Demented,3,1435,F,R,73,12,4,16,1,1478,0.696,1.188
176 | OAS2_0080,OAS2_0080_MR1,Demented,1,0,M,R,66,15,2,25,0.5,1548,0.727,1.134
177 | OAS2_0080,OAS2_0080_MR2,Demented,2,580,M,R,68,15,2,30,0.5,1556,0.713,1.128
178 | OAS2_0080,OAS2_0080_MR3,Demented,3,1209,M,R,69,15,2,28,0.5,1546,0.724,1.135
179 | OAS2_0081,OAS2_0081_MR1,Demented,1,0,F,R,82,12,4,26,0.5,1271,0.695,1.381
180 | OAS2_0081,OAS2_0081_MR2,Demented,2,659,F,R,84,12,4,26,0.5,1273,0.686,1.378
181 | OAS2_0085,OAS2_0085_MR1,Nondemented,1,0,F,R,78,8,5,29,0,1383,0.756,1.269
182 | OAS2_0085,OAS2_0085_MR2,Nondemented,2,670,F,R,80,8,5,27,0,1381,0.751,1.270
183 | OAS2_0086,OAS2_0086_MR1,Nondemented,1,0,F,R,63,15,2,28,0,1544,0.805,1.136
184 | OAS2_0086,OAS2_0086_MR2,Nondemented,2,802,F,R,65,15,2,28,0,1542,0.792,1.138
185 | OAS2_0087,OAS2_0087_MR1,Demented,1,0,F,R,96,17,1,26,1,1465,0.683,1.198
186 | OAS2_0087,OAS2_0087_MR2,Demented,2,754,F,R,98,17,1,21,2,1503,0.660,1.168
187 | OAS2_0088,OAS2_0088_MR1,Demented,1,0,M,R,78,12,4,21,1,1477,0.672,1.188
188 | OAS2_0088,OAS2_0088_MR2,Demented,2,751,M,R,80,12,4,20,1,1494,0.661,1.175
189 | OAS2_0089,OAS2_0089_MR1,Demented,1,0,M,R,70,12,2,29,0.5,1432,0.692,1.225
190 | OAS2_0089,OAS2_0089_MR3,Demented,3,563,M,R,72,12,2,27,1,1432,0.684,1.226
191 | OAS2_0090,OAS2_0090_MR1,Nondemented,1,0,M,R,73,18,2,29,0,1548,0.773,1.134
192 | OAS2_0090,OAS2_0090_MR2,Nondemented,2,680,M,R,75,18,2,29,0,1534,0.772,1.144
193 | OAS2_0090,OAS2_0090_MR3,Nondemented,3,1345,M,R,76,18,2,30,0,1550,0.758,1.133
194 | OAS2_0091,OAS2_0091_MR1,Nondemented,1,0,M,R,75,12,4,28,0,1511,0.739,1.162
195 | OAS2_0091,OAS2_0091_MR2,Nondemented,2,1047,M,R,78,12,4,29,0,1506,0.715,1.166
196 | OAS2_0092,OAS2_0092_MR1,Converted,1,0,F,R,83,12,2,28,0,1383,0.748,1.269
197 | OAS2_0092,OAS2_0092_MR2,Converted,2,706,F,R,84,12,2,27,0.5,1390,0.728,1.263
198 | OAS2_0094,OAS2_0094_MR1,Nondemented,1,0,F,R,61,16,1,30,0,1513,0.771,1.160
199 | OAS2_0094,OAS2_0094_MR2,Nondemented,2,817,F,R,63,16,1,30,0,1449,0.774,1.212
200 | OAS2_0095,OAS2_0095_MR1,Nondemented,1,0,M,R,71,18,1,30,0,1769,0.699,0.992
201 | OAS2_0095,OAS2_0095_MR2,Nondemented,2,673,M,R,72,18,1,29,0,1785,0.687,0.983
202 | OAS2_0095,OAS2_0095_MR3,Nondemented,3,1412,M,R,74,18,1,29,0,1814,0.679,0.967
203 | OAS2_0096,OAS2_0096_MR1,Nondemented,1,0,F,R,89,13,3,29,0,1154,0.750,1.521
204 | OAS2_0096,OAS2_0096_MR2,Nondemented,2,778,F,R,91,13,3,28,0,1165,0.736,1.506
205 | OAS2_0097,OAS2_0097_MR1,Nondemented,1,0,M,R,74,16,2,30,0,1611,0.729,1.089
206 | OAS2_0097,OAS2_0097_MR2,Nondemented,2,1024,M,R,77,16,2,30,0,1628,0.709,1.078
207 | OAS2_0098,OAS2_0098_MR1,Demented,1,0,M,R,66,12,4,30,0.5,1446,0.780,1.214
208 | OAS2_0098,OAS2_0098_MR2,Demented,2,661,M,R,67,12,4,28,0.5,1412,0.783,1.243
209 | OAS2_0099,OAS2_0099_MR1,Demented,1,0,F,R,80,12,,27,0.5,1475,0.762,1.190
210 | OAS2_0099,OAS2_0099_MR2,Demented,2,807,F,R,83,12,,23,0.5,1484,0.750,1.183
211 | OAS2_0100,OAS2_0100_MR1,Nondemented,1,0,F,R,77,11,4,29,0,1583,0.777,1.108
212 | OAS2_0100,OAS2_0100_MR2,Nondemented,2,1218,F,R,80,11,4,30,0,1586,0.757,1.107
213 | OAS2_0100,OAS2_0100_MR3,Nondemented,3,1752,F,R,82,11,4,30,0,1590,0.760,1.104
214 | OAS2_0101,OAS2_0101_MR1,Nondemented,1,0,F,R,71,18,2,30,0,1371,0.769,1.280
215 | OAS2_0101,OAS2_0101_MR2,Nondemented,2,952,F,R,74,18,2,30,0,1400,0.752,1.254
216 | OAS2_0101,OAS2_0101_MR3,Nondemented,3,1631,F,R,76,18,2,30,0,1379,0.757,1.273
217 | OAS2_0102,OAS2_0102_MR1,Demented,1,0,M,R,82,15,3,29,0.5,1499,0.689,1.171
218 | OAS2_0102,OAS2_0102_MR2,Demented,2,610,M,R,84,15,3,29,0.5,1497,0.686,1.172
219 | OAS2_0102,OAS2_0102_MR3,Demented,3,1387,M,R,86,15,3,30,0.5,1498,0.681,1.171
220 | OAS2_0103,OAS2_0103_MR1,Converted,1,0,F,R,69,16,1,30,0,1404,0.750,1.250
221 | OAS2_0103,OAS2_0103_MR2,Converted,2,1554,F,R,74,16,1,30,0.5,1423,0.722,1.233
222 | OAS2_0103,OAS2_0103_MR3,Converted,3,2002,F,R,75,16,1,30,0.5,1419,0.731,1.236
223 | OAS2_0104,OAS2_0104_MR1,Demented,1,0,M,R,70,16,1,25,0.5,1568,0.696,1.119
224 | OAS2_0104,OAS2_0104_MR2,Demented,2,465,M,R,71,16,1,17,1,1562,0.685,1.123
225 | OAS2_0105,OAS2_0105_MR1,Nondemented,1,0,M,R,86,12,4,29,0,1783,0.703,0.984
226 | OAS2_0105,OAS2_0105_MR2,Nondemented,2,675,M,R,87,12,4,30,0,1762,0.718,0.996
227 | OAS2_0106,OAS2_0106_MR1,Demented,1,0,F,R,70,11,4,22,1,1445,0.722,1.214
228 | OAS2_0106,OAS2_0106_MR2,Demented,2,729,F,R,72,11,4,21,1,1489,0.686,1.179
229 | OAS2_0108,OAS2_0108_MR1,Demented,1,0,M,R,77,18,1,25,0.5,1604,0.781,1.094
230 | OAS2_0108,OAS2_0108_MR2,Demented,2,883,M,R,79,18,1,27,0.5,1569,0.781,1.118
231 | OAS2_0109,OAS2_0109_MR1,Nondemented,1,0,M,R,81,11,4,28,0,1750,0.670,1.003
232 | OAS2_0109,OAS2_0109_MR2,Nondemented,2,766,M,R,83,11,4,29,0,1744,0.670,1.006
233 | OAS2_0111,OAS2_0111_MR1,Demented,1,0,M,R,62,12,4,17,0.5,1525,0.732,1.151
234 | OAS2_0111,OAS2_0111_MR2,Demented,2,881,M,R,65,12,4,17,0.5,1520,0.699,1.155
235 | OAS2_0112,OAS2_0112_MR1,Demented,1,0,F,R,76,12,3,27,0.5,1315,0.698,1.335
236 | OAS2_0112,OAS2_0112_MR2,Demented,2,558,F,R,78,12,3,20,0.5,1339,0.689,1.311
237 | OAS2_0113,OAS2_0113_MR1,Demented,1,0,F,R,73,13,2,23,0.5,1536,0.725,1.142
238 | OAS2_0113,OAS2_0113_MR2,Demented,2,504,F,R,75,13,2,28,0.5,1520,0.708,1.155
239 | OAS2_0114,OAS2_0114_MR1,Demented,1,0,F,R,76,12,,27,0.5,1316,0.727,1.333
240 | OAS2_0114,OAS2_0114_MR2,Demented,2,570,F,R,78,12,,27,1,1309,0.709,1.341
241 | OAS2_0116,OAS2_0116_MR1,Demented,1,0,F,R,73,12,3,27,0.5,1425,0.769,1.232
242 | OAS2_0116,OAS2_0116_MR2,Demented,2,616,F,R,75,12,3,28,0.5,1407,0.770,1.247
243 | OAS2_0117,OAS2_0117_MR1,Nondemented,1,0,M,R,73,20,2,30,0,1842,0.758,0.953
244 | OAS2_0117,OAS2_0117_MR2,Nondemented,2,576,M,R,74,20,2,30,0,1806,0.759,0.972
245 | OAS2_0117,OAS2_0117_MR3,Nondemented,3,1345,M,R,76,20,2,30,0,1823,0.739,0.963
246 | OAS2_0117,OAS2_0117_MR4,Nondemented,4,1927,M,R,78,20,2,29,0,1826,0.734,0.961
247 | OAS2_0118,OAS2_0118_MR1,Converted,1,0,F,R,67,14,4,30,0,1508,0.794,1.164
248 | OAS2_0118,OAS2_0118_MR2,Converted,2,1422,F,R,71,14,4,26,0.5,1529,0.788,1.147
249 | OAS2_0119,OAS2_0119_MR1,Nondemented,1,0,F,R,81,15,2,28,0,1486,0.754,1.181
250 | OAS2_0119,OAS2_0119_MR2,Nondemented,2,733,F,R,83,15,2,29,0,1482,0.751,1.184
251 | OAS2_0119,OAS2_0119_MR3,Nondemented,3,1713,F,R,85,15,2,30,0,1488,0.741,1.180
252 | OAS2_0120,OAS2_0120_MR1,Demented,1,0,F,R,76,14,3,25,1,1409,0.715,1.246
253 | OAS2_0120,OAS2_0120_MR2,Demented,2,595,F,R,78,14,3,15,2,1401,0.700,1.253
254 | OAS2_0121,OAS2_0121_MR1,Nondemented,1,0,F,R,73,11,4,30,0,1475,0.726,1.190
255 | OAS2_0121,OAS2_0121_MR2,Nondemented,2,647,F,R,74,11,4,30,0,1517,0.705,1.157
256 | OAS2_0122,OAS2_0122_MR1,Nondemented,1,0,F,R,86,16,3,30,0,1293,0.747,1.357
257 | OAS2_0122,OAS2_0122_MR2,Nondemented,2,597,F,R,88,16,3,30,0,1295,0.744,1.355
258 | OAS2_0124,OAS2_0124_MR1,Demented,1,0,M,R,70,16,3,29,0.5,1463,0.749,1.200
259 | OAS2_0124,OAS2_0124_MR2,Demented,2,472,M,R,71,16,3,27,0.5,1479,0.750,1.187
260 | OAS2_0126,OAS2_0126_MR1,Nondemented,1,0,F,R,74,12,3,29,0,1344,0.739,1.306
261 | OAS2_0126,OAS2_0126_MR2,Nondemented,2,472,F,R,75,12,3,29,0,1338,0.747,1.312
262 | OAS2_0126,OAS2_0126_MR3,Nondemented,3,1192,F,R,77,12,3,29,0,1344,0.740,1.306
263 | OAS2_0127,OAS2_0127_MR1,Converted,1,0,M,R,79,18,1,29,0,1644,0.729,1.067
264 | OAS2_0127,OAS2_0127_MR2,Converted,2,851,M,R,81,18,1,29,0.5,1654,0.720,1.061
265 | OAS2_0127,OAS2_0127_MR3,Converted,3,1042,M,R,81,18,1,29,0.5,1647,0.717,1.066
266 | OAS2_0127,OAS2_0127_MR4,Converted,4,2153,M,R,84,18,1,29,0.5,1668,0.694,1.052
267 | OAS2_0127,OAS2_0127_MR5,Converted,5,2639,M,R,86,18,1,30,0.5,1670,0.669,1.051
268 | OAS2_0128,OAS2_0128_MR1,Nondemented,1,0,F,R,76,16,1,28,0,1346,0.762,1.304
269 | OAS2_0128,OAS2_0128_MR2,Nondemented,2,1140,F,R,79,16,1,29,0,1354,0.739,1.297
270 | OAS2_0129,OAS2_0129_MR1,Nondemented,1,0,F,R,78,18,1,30,0,1440,0.666,1.219
271 | OAS2_0129,OAS2_0129_MR2,Nondemented,2,737,F,R,80,18,1,30,0,1436,0.663,1.222
272 | OAS2_0129,OAS2_0129_MR3,Nondemented,3,1591,F,R,82,18,1,29,0,1442,0.644,1.217
273 | OAS2_0131,OAS2_0131_MR1,Converted,1,0,F,R,65,12,2,30,0.5,1340,0.754,1.309
274 | OAS2_0131,OAS2_0131_MR2,Converted,2,679,F,R,67,12,2,25,0,1331,0.761,1.318
275 | OAS2_0133,OAS2_0133_MR1,Converted,1,0,F,R,78,12,3,29,0,1475,0.731,1.190
276 | OAS2_0133,OAS2_0133_MR3,Converted,3,1006,F,R,81,12,3,28,0.5,1495,0.687,1.174
277 | OAS2_0134,OAS2_0134_MR1,Demented,1,0,F,R,70,11,4,29,0.5,1295,0.748,1.355
278 | OAS2_0134,OAS2_0134_MR2,Demented,2,539,F,R,71,11,4,28,0.5,1284,0.741,1.367
279 | OAS2_0135,OAS2_0135_MR1,Nondemented,1,0,M,R,74,18,2,30,0,1636,0.680,1.073
280 | OAS2_0135,OAS2_0135_MR2,Nondemented,2,1146,M,R,78,18,2,27,0,1645,0.663,1.067
281 | OAS2_0137,OAS2_0137_MR1,Demented,1,0,M,R,74,18,2,28,0.5,1659,0.739,1.058
282 | OAS2_0137,OAS2_0137_MR2,Demented,2,636,M,R,75,18,2,30,0.5,1651,0.737,1.063
283 | OAS2_0138,OAS2_0138_MR1,Nondemented,1,0,F,R,73,16,2,29,0,1123,0.786,1.563
284 | OAS2_0138,OAS2_0138_MR2,Nondemented,2,846,F,R,75,16,2,28,0,1106,0.767,1.587
285 | OAS2_0139,OAS2_0139_MR1,Demented,1,0,F,R,67,16,1,29,0.5,1337,0.766,1.312
286 | OAS2_0139,OAS2_0139_MR2,Demented,2,403,F,R,68,16,1,29,0.5,1344,0.733,1.305
287 | OAS2_0140,OAS2_0140_MR1,Demented,1,0,F,R,76,16,3,26,0.5,1391,0.705,1.262
288 | OAS2_0140,OAS2_0140_MR2,Demented,2,793,F,R,78,16,3,27,0.5,1393,0.690,1.260
289 | OAS2_0140,OAS2_0140_MR3,Demented,3,1655,F,R,81,16,3,25,0.5,1396,0.687,1.257
290 | OAS2_0141,OAS2_0141_MR1,Nondemented,1,0,F,R,65,18,2,30,0,1277,0.812,1.374
291 | OAS2_0141,OAS2_0141_MR2,Nondemented,2,1022,F,R,68,18,2,29,0,1290,0.795,1.361
292 | OAS2_0142,OAS2_0142_MR1,Nondemented,1,0,F,R,69,16,3,29,0,1380,0.819,1.272
293 | OAS2_0142,OAS2_0142_MR2,Nondemented,2,665,F,R,71,16,3,28,0,1390,0.810,1.262
294 | OAS2_0143,OAS2_0143_MR1,Nondemented,1,0,F,R,89,18,2,30,0,1715,0.746,1.023
295 | OAS2_0143,OAS2_0143_MR2,Nondemented,2,561,F,R,91,18,2,30,0,1714,0.741,1.024
296 | OAS2_0143,OAS2_0143_MR3,Nondemented,3,1553,F,R,93,18,2,29,0,1744,0.723,1.006
297 | OAS2_0144,OAS2_0144_MR1,Converted,1,0,M,R,77,16,1,30,0,1704,0.716,1.030
298 | OAS2_0144,OAS2_0144_MR2,Converted,2,683,M,R,79,16,1,30,0.5,1722,0.708,1.019
299 | OAS2_0145,OAS2_0145_MR1,Converted,1,0,F,R,68,16,3,30,0,1298,0.799,1.352
300 | OAS2_0145,OAS2_0145_MR2,Converted,2,1707,F,R,73,16,3,29,0.5,1287,0.771,1.364
301 | OAS2_0146,OAS2_0146_MR1,Demented,1,0,F,R,80,15,2,20,1,1732,0.685,1.013
302 | OAS2_0146,OAS2_0146_MR2,Demented,2,525,F,R,82,15,2,20,1,1729,0.698,1.015
303 | OAS2_0147,OAS2_0147_MR1,Nondemented,1,0,F,R,77,13,2,29,0,1351,0.769,1.299
304 | OAS2_0147,OAS2_0147_MR2,Nondemented,2,440,F,R,78,13,2,29,0,1334,0.769,1.316
305 | OAS2_0147,OAS2_0147_MR3,Nondemented,3,1204,F,R,80,13,2,28,0,1337,0.762,1.313
306 | OAS2_0147,OAS2_0147_MR4,Nondemented,4,1806,F,R,82,13,2,30,0,1342,0.747,1.307
307 | OAS2_0149,OAS2_0149_MR1,Nondemented,1,0,F,R,81,13,2,29,0,1345,0.737,1.305
308 | OAS2_0149,OAS2_0149_MR2,Nondemented,2,674,F,R,83,13,2,30,0,1335,0.732,1.314
309 | OAS2_0150,OAS2_0150_MR1,Demented,1,0,F,R,73,12,3,30,0.5,1343,0.720,1.306
310 | OAS2_0150,OAS2_0150_MR2,Demented,2,518,F,R,75,12,3,27,1,1357,0.714,1.293
311 | OAS2_0152,OAS2_0152_MR1,Nondemented,1,0,F,R,66,18,2,29,0,1191,0.785,1.474
312 | OAS2_0152,OAS2_0152_MR2,Nondemented,2,790,F,R,68,18,2,29,0,1194,0.772,1.469
313 | OAS2_0152,OAS2_0152_MR3,Nondemented,3,1329,F,R,69,18,2,29,0,1202,0.770,1.461
314 | OAS2_0154,OAS2_0154_MR1,Nondemented,1,0,F,R,75,18,1,29,0,1436,0.750,1.222
315 | OAS2_0154,OAS2_0154_MR2,Nondemented,2,791,F,R,77,18,1,28,0,1559,0.713,1.125
316 | OAS2_0156,OAS2_0156_MR1,Nondemented,1,0,F,R,78,18,1,30,0,1243,0.748,1.412
317 | OAS2_0156,OAS2_0156_MR2,Nondemented,2,777,F,R,81,18,1,30,0,1256,0.739,1.398
318 | OAS2_0157,OAS2_0157_MR1,Demented,1,0,F,R,73,12,2,19,1,1274,0.728,1.377
319 | OAS2_0157,OAS2_0157_MR2,Demented,2,764,F,R,75,12,2,18,1,1479,0.657,1.187
320 | OAS2_0158,OAS2_0158_MR1,Nondemented,1,0,F,R,73,15,4,29,0,1272,0.697,1.380
321 | OAS2_0158,OAS2_0158_MR2,Nondemented,2,1399,F,R,76,15,4,29,0,1281,0.680,1.370
322 | OAS2_0159,OAS2_0159_MR1,Demented,1,0,F,R,73,14,3,29,0.5,1238,0.757,1.418
323 | OAS2_0159,OAS2_0159_MR2,Demented,2,759,F,R,76,14,3,28,0.5,1236,0.764,1.419
324 | OAS2_0160,OAS2_0160_MR1,Demented,1,0,M,R,76,12,,27,0.5,1557,0.705,1.127
325 | OAS2_0160,OAS2_0160_MR2,Demented,2,552,M,R,78,12,,29,1,1569,0.704,1.119
326 | OAS2_0161,OAS2_0161_MR1,Nondemented,1,0,M,R,77,16,1,29,0,1818,0.734,0.965
327 | OAS2_0161,OAS2_0161_MR2,Nondemented,2,454,M,R,79,16,1,30,0,1817,0.736,0.966
328 | OAS2_0161,OAS2_0161_MR3,Nondemented,3,1033,M,R,80,16,1,29,0,1830,0.724,0.959
329 | OAS2_0162,OAS2_0162_MR1,Demented,1,0,M,R,82,14,2,23,0.5,1514,0.678,1.159
330 | OAS2_0162,OAS2_0162_MR2,Demented,2,621,M,R,84,14,2,22,0.5,1550,0.665,1.132
331 | OAS2_0164,OAS2_0164_MR1,Demented,1,0,M,R,77,20,1,23,1,1713,0.756,1.024
332 | OAS2_0164,OAS2_0164_MR2,Demented,2,580,M,R,79,20,1,25,2,1710,0.760,1.026
333 | OAS2_0165,OAS2_0165_MR1,Demented,1,0,M,R,78,12,3,23,1,1491,0.710,1.177
334 | OAS2_0165,OAS2_0165_MR2,Demented,2,736,M,R,80,12,3,17,1,1755,0.696,1.000
335 | OAS2_0169,OAS2_0169_MR1,Nondemented,1,0,F,R,71,18,1,30,0,1426,0.731,1.231
336 | OAS2_0169,OAS2_0169_MR2,Nondemented,2,691,F,R,73,18,1,30,0,1414,0.739,1.241
337 | OAS2_0171,OAS2_0171_MR1,Nondemented,1,0,M,R,76,16,3,30,0,1832,0.769,0.958
338 | OAS2_0171,OAS2_0171_MR2,Nondemented,2,493,M,R,77,16,3,30,0,1820,0.768,0.964
339 | OAS2_0171,OAS2_0171_MR3,Nondemented,3,1695,M,R,81,16,3,30,0,1836,0.744,0.956
340 | OAS2_0172,OAS2_0172_MR1,Demented,1,0,M,R,75,16,1,30,0.5,1891,0.709,0.928
341 | OAS2_0172,OAS2_0172_MR2,Demented,2,1212,M,R,79,16,1,29,0.5,1899,0.700,0.924
342 | OAS2_0174,OAS2_0174_MR1,Nondemented,1,0,M,R,60,12,4,30,0,1379,0.806,1.273
343 | OAS2_0174,OAS2_0174_MR2,Nondemented,2,695,M,R,62,12,4,30,0,1378,0.795,1.274
344 | OAS2_0174,OAS2_0174_MR3,Nondemented,3,1555,M,R,64,12,4,30,0,1370,0.794,1.281
345 | OAS2_0175,OAS2_0175_MR1,Demented,1,0,M,R,70,16,4,26,0.5,1796,0.742,0.977
346 | OAS2_0175,OAS2_0175_MR2,Demented,2,700,M,R,72,16,4,28,0.5,1796,0.732,0.977
347 | OAS2_0175,OAS2_0175_MR3,Demented,3,1343,M,R,73,16,4,28,0.5,1803,0.731,0.973
348 | OAS2_0176,OAS2_0176_MR1,Converted,1,0,M,R,84,16,2,30,0,1404,0.710,1.250
349 | OAS2_0176,OAS2_0176_MR2,Converted,2,774,M,R,87,16,2,30,0,1398,0.696,1.255
350 | OAS2_0176,OAS2_0176_MR3,Converted,3,1631,M,R,89,16,2,30,0.5,1408,0.679,1.246
351 | OAS2_0177,OAS2_0177_MR1,Nondemented,1,0,M,R,68,14,3,26,0,1444,0.778,1.216
352 | OAS2_0177,OAS2_0177_MR2,Nondemented,2,665,M,R,70,14,3,28,0,1510,0.770,1.162
353 | OAS2_0178,OAS2_0178_MR1,Nondemented,1,0,F,R,89,14,2,29,0,1509,0.756,1.163
354 | OAS2_0178,OAS2_0178_MR2,Nondemented,2,600,F,R,90,14,2,28,0,1495,0.746,1.174
355 | OAS2_0178,OAS2_0178_MR3,Nondemented,3,1447,F,R,93,14,2,30,0,1488,0.735,1.179
356 | OAS2_0179,OAS2_0179_MR1,Demented,1,0,M,R,79,20,1,26,0.5,1548,0.711,1.134
357 | OAS2_0179,OAS2_0179_MR2,Demented,2,652,M,R,81,20,1,26,0.5,1556,0.691,1.128
358 | OAS2_0181,OAS2_0181_MR1,Demented,1,0,F,R,74,12,,26,0.5,1171,0.733,1.499
359 | OAS2_0181,OAS2_0181_MR2,Demented,2,539,F,R,75,12,,,1,1169,0.742,1.501
360 | OAS2_0181,OAS2_0181_MR3,Demented,3,1107,F,R,77,12,,,1,1159,0.733,1.515
361 | OAS2_0182,OAS2_0182_MR1,Demented,1,0,M,R,73,12,,23,0.5,1661,0.698,1.056
362 | OAS2_0182,OAS2_0182_MR2,Demented,2,776,M,R,75,12,,20,0.5,1654,0.696,1.061
363 | OAS2_0183,OAS2_0183_MR1,Nondemented,1,0,F,R,66,13,2,30,0,1495,0.746,1.174
364 | OAS2_0183,OAS2_0183_MR2,Nondemented,2,182,F,R,66,13,2,30,0,1506,0.740,1.165
365 | OAS2_0183,OAS2_0183_MR3,Nondemented,3,732,F,R,68,13,2,30,0,1506,0.740,1.165
366 | OAS2_0183,OAS2_0183_MR4,Nondemented,4,2107,F,R,72,13,2,30,0,1510,0.723,1.162
367 | OAS2_0184,OAS2_0184_MR1,Demented,1,0,F,R,72,16,3,24,0.5,1354,0.733,1.296
368 | OAS2_0184,OAS2_0184_MR2,Demented,2,553,F,R,73,16,3,21,1,1351,0.708,1.299
369 | OAS2_0185,OAS2_0185_MR1,Demented,1,0,M,R,80,16,1,28,0.5,1704,0.711,1.030
370 | OAS2_0185,OAS2_0185_MR2,Demented,2,842,M,R,82,16,1,28,0.5,1693,0.694,1.037
371 | OAS2_0185,OAS2_0185_MR3,Demented,3,2297,M,R,86,16,1,26,0.5,1688,0.675,1.040
372 | OAS2_0186,OAS2_0186_MR1,Nondemented,1,0,F,R,61,13,2,30,0,1319,0.801,1.331
373 | OAS2_0186,OAS2_0186_MR2,Nondemented,2,763,F,R,63,13,2,30,0,1327,0.796,1.323
374 | OAS2_0186,OAS2_0186_MR3,Nondemented,3,1608,F,R,65,13,2,30,0,1333,0.801,1.317
375 |
--------------------------------------------------------------------------------
/DataCheckpoint_Group141_WI24.ipynb:
--------------------------------------------------------------------------------
1 | {
2 | "cells": [
3 | {
4 | "cell_type": "markdown",
5 | "metadata": {},
6 | "source": [
7 | "# COGS 108 - Data Checkpoint"
8 | ]
9 | },
10 | {
11 | "cell_type": "markdown",
12 | "metadata": {},
13 | "source": [
14 | "# Names\n",
15 | "\n",
16 | "- Shivangi Gupta\n",
17 | "- Joseph Hwang\n",
18 | "- Zijun Yang\n",
19 | "- Johnny Gonzales\n",
20 | "- Tanishq Rathore"
21 | ]
22 | },
23 | {
24 | "cell_type": "markdown",
25 | "metadata": {},
26 | "source": [
27 | "# Research Question"
28 | ]
29 | },
30 | {
31 | "cell_type": "markdown",
32 | "metadata": {},
33 | "source": [
34 | "Utilizing clinical MRI Data and personal details of an individual, can we predict via machine learning model whether an individual will have an onset of Alzheimer's disease? Features the model will be trained on include variables such as Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), gender, age, years of education, socioeconomic status, Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and Atlas Scaling Factor (ASF)."
35 | ]
36 | },
37 | {
38 | "cell_type": "markdown",
39 | "metadata": {},
40 | "source": [
41 | "## Background and Prior Work"
42 | ]
43 | },
44 | {
45 | "cell_type": "markdown",
46 | "metadata": {},
47 | "source": [
48 | "Advancements in healthcare, improvements in living conditions, and\n",
49 | "breakthroughs in medicine have collectively contributed to longer life\n",
50 | "expectancies worldwide; simultaneously, developed countries are also\n",
51 | "experiencing declining fertility\n",
52 | "rates.[1](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
53 | "The combination of these two circumstances has resulted in the\n",
54 | "proportion of older people within populations to steadily increase. The\n",
55 | "World Health Organization (WHO) reported that “in 2020, the number of\n",
56 | "people aged 60 and older outnumbered children younger than 5\n",
57 | "years”.[2](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
58 | "In addition, they also state that “between 2015 and 2050, the proportion\n",
59 | "of the world’s population over 60 years will nearly double from 12% to\n",
60 | "22%”. As a result, it is reasonable that we examine common health\n",
61 | "conditions associated with older age, one being Alzheimer’s disease.\n",
62 | "\n",
63 | "So what is Alzheimer’s disease? Alzheimer’s disease is a progressive\n",
64 | "neurodegenerative brain disorder that impairs memory and cognitive\n",
65 | "functions. It is the most common cause of dementia and affects about 6.5\n",
66 | "million people in the United States who are aged 65 and\n",
67 | "older.[3](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
68 | "At the moment, there are no cures for the disease but medicines may\n",
69 | "improve or slow the progression of\n",
70 | "symptoms.[3](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
71 | "As such, it is our project to create a model that is able to predict\n",
72 | "Alzheimer’s disease based on clinical data that include factors that\n",
73 | "show risk and progression of the disease.\n",
74 | "\n",
75 | "There are several other projects that have asked similar questions and\n",
76 | "approached similar problems for other diseases. For instance, one study\n",
77 | "tried to use machine learning methods to predict risk of cardiovascular\n",
78 | "disease based on major contributing\n",
79 | "factors.[4](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/)\n",
80 | "Similarly, another paper used machine learning and ranker-based feature\n",
81 | "selection methods to predict eye diseases based on\n",
82 | "symptoms.[5](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
83 | "Lastly, there was a paper that predicted thyroid disease using selective\n",
84 | "features and machine learning\n",
85 | "techniques.[6](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/)\n",
86 | "All three papers seem to have been relatively successful in predicting\n",
87 | "the disease based on distinct features. Evidently, training machine\n",
88 | "learning models based on datasets which contain factors and indicators\n",
89 | "for a given disease is not a novel format of question and method; we\n",
90 | "hope to achieve similarly for Alzheimer’s disease.\n",
91 | "\n",
92 | "In-Depth Study Analysis\n",
93 | "\n",
94 | "Our group analyzed two studies published in the National Institute of\n",
95 | "Health’s (NIH) journal database. The first study[7](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8927715/) employed\n",
96 | "machine learning models to predict early-stage Alzheimer's Disease using\n",
97 | "Open Access Series of Imaging Studies (OASIS) data, focusing on metrics\n",
98 | "like precision, recall, accuracy, and F1-score. The authors, with\n",
99 | "backgrounds in technology and health research, aimed to enhance early\n",
100 | "diagnosis, potentially lowering Alzheimer's mortality rates. The study\n",
101 | "demonstrated that machine learning techniques such as decision trees,\n",
102 | "random forests, SVM, gradient boosting, and voting classifiers can\n",
103 | "effectively predict early-stage Alzheimer's Disease with an accuracy of\n",
104 | "up to 83%. This achievement highlights the critical role of data science\n",
105 | "in identifying Alzheimer's at an early phase, leveraging feature\n",
106 | "selection and advanced algorithms to enhance diagnostic accuracy. Early\n",
107 | "detection is crucial for timely intervention, potentially mitigating the\n",
108 | "disease's progression and impact on patients and their families (Kavitha\n",
109 | "et al.).\n",
110 | "\n",
111 | "The second study[8](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) titled\n",
112 | "“Application of machine learning methods for diagnosis of dementia based\n",
113 | "on the 10/66 battery of cognitive function tests in south India”\n",
114 | "investigated the use of machine learning for diagnosing dementia in\n",
115 | "South India, employing the culturally and educationally fair 10/66\n",
116 | "battery of cognitive function tests designed for use in low and\n",
117 | "middle-income countries. Through the analysis of neuropsychological\n",
118 | "data, demographic information, and normative data, the research applied\n",
119 | "Jrip classification algorithm among others, achieving high diagnostic\n",
120 | "accuracy. This approach demonstrates the potential to streamline the\n",
121 | "diagnostic process, making it quicker and more accessible for clinicians\n",
122 | "and patients in India, thereby addressing the significant healthcare\n",
123 | "challenge of efficiently identifying dementia in community settings\n",
124 | "(Bhagyashree et al).\n",
125 | "\n",
126 | "In-Depth Analysis of Similar Projects\n",
127 | "\n",
128 | "Our group also delved into actual Kaggle projects that are directly\n",
129 | "associated with the dataset we’ve chosen to use, delving into EDA and\n",
130 | "prediction models using Scikit-Learn and Tensorflow. The first\n",
131 | "project[9](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
132 | "I will discuss starts with an introduction to Alzheimer's disease and\n",
133 | "the problem statement of estimating the Clinical Dementia Rating (CDR)\n",
134 | "using MRI dataset features. It progresses through data loading and\n",
135 | "preprocessing, including null value handling and normalization, and\n",
136 | "employs machine learning techniques, specifically mentioning model\n",
137 | "training with hyperparameter tuning for XGBClassifier and\n",
138 | "GradientBoostingClassifier. The notebook concludes with predictions and\n",
139 | "performance evaluation, indicated by confusion matrix and classification\n",
140 | "report visualizations, and the model was able to reach a final accuracy\n",
141 | "of \\~80%.\n",
142 | "\n",
143 | "The second project[10](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) tried to predict the Clinical Rating of Alzheimer's disease (CRA) by\n",
144 | "integrating data loading, visualization, and extensive machine learning,\n",
145 | "including the use of TensorFlow for neural network models. It explored\n",
146 | "various machine learning models, with a special emphasis on model\n",
147 | "training and evaluation, culminating in the finding that the\n",
148 | "DecisionTreeClassifier performed the best among the models tested. The\n",
149 | "conclusion stressed the need for more data to enhance the precision of\n",
150 | "Alzheimer's disease predictions, highlighting the challenge of data\n",
151 | "scarcity in achieving accurate diagnostic models.\n",
152 | "\n",
153 | "**References**\n",
154 | "\n",
155 | "1. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/) Nargund G. (2009) Declining birth rate in Developed Countries: A\n",
156 | " radical policy re-think is required. *Facts, views & vision in\n",
157 | " ObGyn, 1(3), 191–193.*\n",
158 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4255510/)\n",
159 | "\n",
160 | "2. [^](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health) World Health Organization. (1 Oct 2022) Ageing and health. *World\n",
161 | " Health Organization*.\n",
162 | " [https://www.who.int/news-room/fact-sheets/detail/ageing-and-health](https://www.who.int/news-room/fact-sheets/detail/ageing-and-health)\n",
163 | "\n",
164 | "3. [^](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447) Mayo Foundation for Medical Education and Research. (30\n",
165 | " August 2023) Alzheimer’s disease. *Mayo Clinic*.\n",
166 | " [https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447](https://www.mayoclinic.org/diseases-conditions/alzheimers-disease/symptoms-causes/syc-20350447)\n",
167 | "\n",
168 | "4. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10036320/) Peng, M., Hou, F., Cheng, Z., Shen, T., Liu, K., Zhao, C., &\n",
169 | " Zheng, W. (23 Mar 2023) Prediction of cardiovascular disease risk\n",
170 | " based on major contributing features. *Scientific reports, 13(1),\n",
171 | " 4778*.\n",
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173 | "\n",
174 | "5. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/) Marouf, A. A., Mottalib, M. M., Alhajj, R., Rokne, J., &\n",
175 | " Jafarullah, O. (24 Dec 2022) An Efficient Approach to Predict Eye\n",
176 | " Diseases from Symptoms Using Machine Learning and Ranker-Based\n",
177 | " Feature Selection Methods. *Bioengineering (Basel, Switzerland),\n",
178 | " 10(1), 25*.\n",
179 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9854513/)\n",
180 | "\n",
181 | "6. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC9405591/) Chaganti, R., Rustam, F., De La Torre Díez, I., Mazón, J. L. V.,\n",
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183 | " Prediction Using Selective Features and Machine Learning\n",
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186 | "\n",
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192 | "\n",
193 | "8. [^](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/) Bhagyashree SIR, Nagaraj K, Prince M, Fall CHD, Krishna M.\n",
194 | " Diagnosis of Dementia by Machine learning methods in\n",
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198 | " PMCID: PMC6138240.\n",
199 | " [https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/](https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6138240/)\n",
200 | "\n",
201 | "9. [^](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri) Reddy, Shreyas. 2021. Alzheimer's analysis using MRI, February\n",
202 | " 8, 2024.\n",
203 | " [https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri](https://www.kaggle.com/code/shreyaspj/alzheimer-s-analysis-using-mri)\n",
204 | "\n",
205 | "10. [^](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf) Andrew. 2017. Predict alzheimer disease sl and tf, February 8, 2024.\n",
206 | " [https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf](https://www.kaggle.com/code/andrew32bit/predict-alzheimer-disease-sl-and-tf)"
207 | ]
208 | },
209 | {
210 | "cell_type": "markdown",
211 | "metadata": {},
212 | "source": [
213 | "# Hypothesis\n"
214 | ]
215 | },
216 | {
217 | "cell_type": "markdown",
218 | "metadata": {},
219 | "source": [
220 | "Our project's hypothesis is the following: \"It is possible to predict the onset of Alzheimers based on the combination of (1) clinical data* and (2) personal features such as gender, age, years of education, and socioeconomic status.\" We believe that we will be able to successfully train a model that is able to predict the onset of Alzheimer's disease because, as mentioned in the background portion of the proposal, there has been numerous successful machine learning models trained on clinical data to predict the onset of a disease. The clinical data provide variables that capture both the cognitive and structural changes associated with the disease's progression. Incorporating personal features such as gender and age in the prediction model is justified by extensive research indicating that these factors can influence the risk and progression rate of Alzheimer's disease. Together, we believe that this will be enough data to train a model to predict the onset of Alzheimer's disease.\n",
221 | "\n",
222 | "*Clinical data include Mini Mental State Examination (MMSE), visit number, Clinical Dementia Rating (CDR), Estimated total intracranial volume (eTIV), Normalize Whole Brain Volume (nWBV), and the Atlas Scaling Factor (ASF)"
223 | ]
224 | },
225 | {
226 | "cell_type": "markdown",
227 | "metadata": {},
228 | "source": [
229 | "# Data"
230 | ]
231 | },
232 | {
233 | "cell_type": "markdown",
234 | "metadata": {},
235 | "source": [
236 | "## Data overview\n",
237 | "\n",
238 | "- Dataset #1\n",
239 | " - Dataset Name: OASIS-2: Longitudinal MRI Data in Nondemented and Demented Older Adults\n",
240 | " - Link to the dataset:https://www.oasis-brains.org/#data\n",
241 | " - Number of observations: 373\n",
242 | " - Number of variables: 15\n",
243 | "\n",
244 | "The dataset provided by The Open Access Series of Imaging Studies (OASIS) contains a collection of 150 subjects aged 60 to 96. Each subject was scanned on 2 or more visits, separated by one year for a total of 373 imaging sessions. All subjects were right-handed and included both men and women. 72 subjects were charaterized as nondemented throughout the study. 64 subjects subjects were characterized as demented at their initial vists and remained so for subsequent scans. Lastly, 14 subjects were characterized as nondemented at their initial vist but were subsequently characterized as demented in later visits. Important variables are years of education (EDUC), socioeconomic status (SES; 1 to 5), mini mental state examination (MMSE score), clinical dementia rating (CDR rating scale), estimated total intracranial volume (eTIV), normalize whole brain volume(nWBV), and atlas scaling factor (ASF). \n",
245 | "\n",
246 | "The variables may be proxies for Alzheimers/cognitive state (conjecture):\n",
247 | "- __EDUC__: Represents years of education which may proxy for cognitive reserve. Higher education levels may result in better cognitive function and reduced risk of dementia. \n",
248 | "- __SES__: Ranging from 1 to 5, is a proxy for income, education level, and occupation, which represents environmental influences on cognitive health. \n",
249 | "- __MMSE__: A widely used screening tool for cognitive impairment. Scores range from 1 to 30 with higher scores indicating better cognitive function. The examination assesses domains such as orientation, memory, attention, and language. \n",
250 | "- __CDR__: CDR scale is also commonly used to assess severity of dementia symptoms. The scale ranges from 0 to 3 where 0 indicates no dementia, 0.5 is questionable dementia, 1 is mild dementia, 2 is moderate dementia, and 3 is severe dementia.\n",
251 | "- __eTIV__: Indicates the total volume inside the skull (including brain tissue, cerebrospinal fluid, etc.). It is measured in cubic centimeters (cc) and can help establish baseline brain size. Decrease/varying sizes may proxy for cognitive function and Alzheimer's risk.\n",
252 | "- __nWBV__: Represents the volume of the brain normalized to the subject's eTIV. It is expressed as a percentage, reflecting the proportion of the brain occupying the eTIV (nWBV = brain volume / eTIV). Changes in nWBV may be indicative of brain atrophy which is a common feature of Alzheimer's and dementia.\n",
253 | "- __ASF__: A scaling factor used in brain imaging to adjust for individual differences in brain size and shape. It accounts for variability in brain morphology allowing for accurate comparisons of brain structures.\n",
254 | "\n",
255 | "The dataset provided by OASIS is relatively clean and organized. However, some cleaning may be required, such as changing M/F and group to numeric values, dropping columns such as MRI ID, Visit, and Hand which will not be used in our predictions. All of this will be done by manipulating the dataframe. "
256 | ]
257 | },
258 | {
259 | "cell_type": "markdown",
260 | "metadata": {},
261 | "source": [
262 | "## Dataset #1: oasis_longitudinal.csv"
263 | ]
264 | },
265 | {
266 | "cell_type": "markdown",
267 | "metadata": {},
268 | "source": [
269 | "### Setup"
270 | ]
271 | },
272 | {
273 | "cell_type": "code",
274 | "execution_count": 1,
275 | "metadata": {},
276 | "outputs": [],
277 | "source": [
278 | "import pandas as pd\n",
279 | "import numpy as np\n",
280 | "import seaborn as sns\n",
281 | "import matplotlib.pyplot as plt\n",
282 | "%matplotlib inline\n",
283 | "%config InlineBackend.figure_format ='retina'"
284 | ]
285 | },
286 | {
287 | "cell_type": "code",
288 | "execution_count": 2,
289 | "metadata": {},
290 | "outputs": [
291 | {
292 | "data": {
293 | "text/html": [
294 | "