├── DIABETECH-INTELLIGENT DEVICE FOR PREDICTING DIABETES.zip
├── .devcontainer
    └── devcontainer.json
├── README.md
└── streamlit_app.py


/DIABETECH-INTELLIGENT DEVICE FOR PREDICTING DIABETES.zip:
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https://raw.githubusercontent.com/santhosh050505/DIABETECH-INTELLIGENT-DEVICE-FOR-PREDICTING-DIABETES/HEAD/DIABETECH-INTELLIGENT DEVICE FOR PREDICTING DIABETES.zip


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/.devcontainer/devcontainer.json:
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 1 | {
 2 |   "name": "Python 3",
 3 |   // Or use a Dockerfile or Docker Compose file. More info: https://containers.dev/guide/dockerfile
 4 |   "image": "mcr.microsoft.com/devcontainers/python:1-3.11-bullseye",
 5 |   "customizations": {
 6 |     "codespaces": {
 7 |       "openFiles": [
 8 |         "README.md",
 9 |         "streamlit_app.py"
10 |       ]
11 |     },
12 |     "vscode": {
13 |       "settings": {},
14 |       "extensions": [
15 |         "ms-python.python",
16 |         "ms-python.vscode-pylance"
17 |       ]
18 |     }
19 |   },
20 |   "updateContentCommand": "[ -f packages.txt ] && sudo apt update && sudo apt upgrade -y && sudo xargs apt install -y <packages.txt; [ -f requirements.txt ] && pip3 install --user -r requirements.txt; pip3 install --user streamlit; echo '✅ Packages installed and Requirements met'",
21 |   "postAttachCommand": {
22 |     "server": "streamlit run streamlit_app.py --server.enableCORS false --server.enableXsrfProtection false"
23 |   },
24 |   "portsAttributes": {
25 |     "8501": {
26 |       "label": "Application",
27 |       "onAutoForward": "openPreview"
28 |     }
29 |   },
30 |   "forwardPorts": [
31 |     8501
32 |   ]
33 | }


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/README.md:
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 1 | 
 2 | 
 3 | DIABETECH: Intelligent Device for Predicting Diabetes is an advanced healthcare solution designed to predict diabetes risk using artificial intelligence and real-time data analysis. The device is a user-friendly, portable system that integrates machine learning algorithms and biometric sensors to deliver accurate, non-invasive, and early detection of diabetes risk factors.
 4 | 
 5 | The key features of DIABETECH include:
 6 | 
 7 | Biometric Data Collection: The device uses sensors to monitor key health parameters such as blood glucose levels, blood pressure, heart rate, BMI, and lifestyle metrics.
 8 | AI-Powered Predictions: Machine learning models analyze collected data to assess the likelihood of prediabetes or diabetes development.
 9 | Personalized Health Insights: The device provides actionable insights and tailored recommendations, including diet plans, exercise routines, and lifestyle modifications, to manage or reduce diabetes risk.
10 | Continuous Monitoring: The device supports continuous health tracking, allowing users to monitor their progress and take proactive steps toward managing their health.
11 | Cloud Connectivity: Data is securely stored and synchronized with cloud platforms, enabling remote access for users and healthcare providers.
12 | DIABETECH empowers individuals with knowledge about their health, facilitates early diagnosis, and helps mitigate the complications associated with diabetes. By leveraging cutting-edge technology, it aims to address the global challenge of diabetes prevalence and promote a healthier, more informed society.
13 | 
14 | This innovation represents a significant leap in preventive healthcare, bridging the gap between medical expertise and accessible health monitoring.
15 | 


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/streamlit_app.py:
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  1 | # Install required libraries
  2 | # pip install streamlit pandas scikit-learn matplotlib seaborn plotly
  3 | 
  4 | # IMPORT STATEMENTS
  5 | import streamlit as st
  6 | import pandas as pd
  7 | import numpy as np
  8 | import matplotlib.pyplot as plt
  9 | import seaborn as sns
 10 | from sklearn.metrics import accuracy_score
 11 | from sklearn.ensemble import RandomForestClassifier
 12 | from sklearn.model_selection import train_test_split
 13 | 
 14 | # LOAD THE DATASET
 15 | # Ensure the `diabetes.csv` file is in the same directory as `streamlit_app.py`
 16 | df = pd.read_csv('diabetes.csv')
 17 | 
 18 | # REMOVE THE PREGNANCIES COLUMN FROM THE DATASET
 19 | df = df.drop('Pregnancies', axis=1)
 20 | 
 21 | # HEADINGS
 22 | st.title('Diabetech - Monitoring Diabetes')
 23 | st.sidebar.header('Patient Data')
 24 | st.subheader('Training Data Overview')
 25 | st.write(df.describe())
 26 | 
 27 | # SPLIT THE DATA INTO X AND Y
 28 | x = df.drop(['Outcome'], axis=1)
 29 | y = df['Outcome']
 30 | x_train, x_test, y_train, y_test = train_test_split(x, y, test_size=0.2, random_state=0)
 31 | 
 32 | # FUNCTION TO GET USER INPUT (WITHOUT PREGNANCIES)
 33 | def user_report():
 34 |     glucose = st.sidebar.slider('Glucose', 0, 200, 120)
 35 |     bp = st.sidebar.slider('Blood Pressure', 0, 122, 70)
 36 |     skinthickness = st.sidebar.slider('Skin Thickness', 0, 100, 20)
 37 |     insulin = st.sidebar.slider('Insulin', 0, 846, 79)
 38 |     bmi = st.sidebar.slider('BMI', 0, 67, 20)
 39 |     dpf = st.sidebar.slider('Diabetes Pedigree Function', 0.0, 2.4, 0.47)
 40 |     age = st.sidebar.slider('Age', 21, 88, 33)
 41 | 
 42 |     # Create a DataFrame with user inputs
 43 |     user_report_data = {
 44 |         'Glucose': glucose,
 45 |         'BloodPressure': bp,
 46 |         'SkinThickness': skinthickness,
 47 |         'Insulin': insulin,
 48 |         'BMI': bmi,
 49 |         'DiabetesPedigreeFunction': dpf,
 50 |         'Age': age
 51 |     }
 52 |     return pd.DataFrame(user_report_data, index=[0])
 53 | 
 54 | # PATIENT DATA
 55 | user_data = user_report()
 56 | st.subheader('Patient Data Input')
 57 | st.write(user_data)
 58 | 
 59 | # TRAINING THE MODEL
 60 | rf = RandomForestClassifier()
 61 | rf.fit(x_train, y_train)
 62 | 
 63 | # PREDICTING THE RESULT
 64 | user_result = rf.predict(user_data)
 65 | 
 66 | # VISUALISATIONS
 67 | st.title('Visualized Patient Report')
 68 | 
 69 | # Function to color based on prediction
 70 | color = 'blue' if user_result[0] == 0 else 'red'
 71 | 
 72 | # Visualization - Age vs Glucose
 73 | st.header('Glucose Value Comparison (Others vs Yours)')
 74 | fig1 = plt.figure()
 75 | sns.scatterplot(x='Age', y='Glucose', data=df, hue='Outcome', palette='magma')
 76 | sns.scatterplot(x=user_data['Age'], y=user_data['Glucose'], s=150, color=color)
 77 | plt.title('0 - Healthy & 1 - Unhealthy')
 78 | st.pyplot(fig1)
 79 | 
 80 | # Visualization - Age vs Blood Pressure
 81 | st.header('Blood Pressure Comparison (Others vs Yours)')
 82 | fig2 = plt.figure()
 83 | sns.scatterplot(x='Age', y='BloodPressure', data=df, hue='Outcome', palette='coolwarm')
 84 | sns.scatterplot(x=user_data['Age'], y=user_data['BloodPressure'], s=150, color=color)
 85 | plt.title('0 - Healthy & 1 - Unhealthy')
 86 | st.pyplot(fig2)
 87 | 
 88 | # Visualization - Age vs BMI
 89 | st.header('BMI Comparison (Others vs Yours)')
 90 | fig3 = plt.figure()
 91 | sns.scatterplot(x='Age', y='BMI', data=df, hue='Outcome', palette='rainbow')
 92 | sns.scatterplot(x=user_data['Age'], y=user_data['BMI'], s=150, color=color)
 93 | plt.title('0 - Healthy & 1 - Unhealthy')
 94 | st.pyplot(fig3)
 95 | 
 96 | # OUTPUT
 97 | st.subheader('Prediction Result')
 98 | output = 'You are not diabetic.' if user_result[0] == 0 else 'You are diabetic.'
 99 | st.title(output)
100 | 
101 | # DISPLAY ACCURACY
102 | st.subheader('Model Accuracy')
103 | accuracy = accuracy_score(y_test, rf.predict(x_test)) * 100
104 | st.write(f"{accuracy:.2f}%")
105 | 


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