├── instructions.md ├── data_dictionary.md ├── heart_analysis.ipynb └── heart.csv /instructions.md: -------------------------------------------------------------------------------- 1 | 2 | --- 3 | applyTo: "notebook" 4 | --- 5 | 6 | ## Notebook Instructions 7 | 8 | * Start a notebook with a title markdown cell such as `# Notebook Title`. 9 | 10 | * The second markdown cell should be `## Python packages` followed by a code cell that imports all necessary Python packages. 11 | 12 | * End this section with a code cell with the Python code `%config InlineBackend.figure_format = 'retina'` to enable high DPI plotting. 13 | 14 | * The third markdown cell should be `## Load data` followed by a code cell that loads the data and subsequent code cells that perform any necessary data cleaning and preprocessing. 15 | 16 | * The next markdown cell should be `## Analysis` followed by code cells that perform the analysis. Follow the instruction in the prompt to complete the analysis. 17 | 18 | ## General guidelines for notebook structure and formatting 19 | 20 | * Use a clear and descriptive title for the notebook. 21 | 22 | * Use markdown section cells to organize the notebook into sections. 23 | 24 | * Use code cells for executable code. 25 | 26 | * Ensure that the code is well-commented and easy to understand. 27 | 28 | * Do not create excessively long code cells. 29 | 30 | * Break code cells into shorter, manageable chunks. 31 | 32 | * Use text and LaTeX in markdown cells after each code cell execution to explain the results of the code. For instance, if the code cell contained the code `df.Age.mean()` and the result is 42, use a markdown cell to write: The sample mean $\bar{X}=42$ years. 33 | 34 | * Use consistent formatting throughout the notebook for a professional appearance. -------------------------------------------------------------------------------- /data_dictionary.md: -------------------------------------------------------------------------------- 1 | --- 2 | Name: Data Dictionary 3 | Description: Metadata about each variable 4 | --- 5 | 6 | ## Data dictionary 7 | 8 | This is a file that states the name of each variable (column header), describes data type of each column in the data file and gives information about each column. 9 | 10 | ## Response variable 11 | 12 | ### HeartDisease 13 | 14 | The “HeartDisease” column is a binary variable with 0 encoding the absence of heart disease and 1 encoding the presence of heart disease. 15 | 16 | ## Explanatory variables 17 | 18 | ### Age 19 | 20 | The “Age” column is a numerical variable and each row is the integer age of a subject measured in years. 21 | 22 | ### Sex 23 | 24 | The “Sex” column is a binary variable with “M” encoding a male and “F” encoding a female subject. 25 | 26 | ### ChestPainType 27 | 28 | The “ChestPainType” column is a nominal variable with four categories. “ATA” encodes atypical chest pain, “ASY” encodes asymptomatic subjects, “NAP” encodes non-angina chest pain, and “TA” encoding transient ischemia. 29 | 30 | ### RestingBP 31 | 32 | The “RestingBP” column is a numerical variable and each row is the integer resting systolic blood pressure of a subject measured in mm Hg. 33 | 34 | ### Cholesterol 35 | 36 | The “Cholesterol” column is a numerical variable and each row is the integer serum total cholesterol of a subject measured in mg/dL. 37 | 38 | ### RestingECG 39 | 40 | The “RestingECG” column is a binary variable with “Normal” encoding a normal ECG and “ST” encoding ST-segment changes on the ECG. 41 | 42 | ### MaxHR 43 | 44 | The “MaxHR” column is a numerical variable and each row represents the integer heart rate of a subject in beats per minute. 45 | 46 | ### ExerciseAngina 47 | 48 | The “ExerciseAngina” column is a binary variable with “N” encoding the absence of exercise-induced angina and “Y” encoding the presence of exercise-induced angina. 49 | 50 | ### ST_Slope 51 | 52 | The “ST_Slope” column is a binary variable with “Flat” indicating a normal ST-segment on the ECG and “Up” encoding ST-segment elevation. -------------------------------------------------------------------------------- /heart_analysis.ipynb: -------------------------------------------------------------------------------- 1 | { 2 | "cells": [ 3 | { 4 | "cell_type": "markdown", 5 | "id": "1", 6 | "metadata": {}, 7 | "source": [ 8 | "# Heart Disease Dataset Analysis" 9 | ] 10 | }, 11 | { 12 | "cell_type": "markdown", 13 | "id": "10oqvetkdct", 14 | "source": "## Python packages", 15 | "metadata": {} 16 | }, 17 | { 18 | "cell_type": "code", 19 | "id": "p1pt8q9hrs", 20 | "source": "import pandas as pd\nimport numpy as np\nimport matplotlib.pyplot as plt\nimport seaborn as sns", 21 | "metadata": {}, 22 | "execution_count": null, 23 | "outputs": [] 24 | }, 25 | { 26 | "cell_type": "code", 27 | "id": "enr3ahnva4c", 28 | "source": "%config InlineBackend.figure_format = 'retina'", 29 | "metadata": {}, 30 | "execution_count": null, 31 | "outputs": [] 32 | }, 33 | { 34 | "cell_type": "markdown", 35 | "id": "j0l8td2uug9", 36 | "source": "## Load data", 37 | "metadata": {} 38 | }, 39 | { 40 | "cell_type": "code", 41 | "id": "n09gbvcjgho", 42 | "source": "df = pd.read_csv('heart.csv')", 43 | "metadata": {}, 44 | "execution_count": null, 45 | "outputs": [] 46 | }, 47 | { 48 | "cell_type": "code", 49 | "id": "wq4rzwqkoh", 50 | "source": "# Display basic information about the dataset\nprint(f\"Dataset shape: {df.shape}\")\nprint(f\"\\nColumn names and types:\")\nprint(df.dtypes)\nprint(f\"\\nFirst few rows:\")\ndf.head()", 51 | "metadata": {}, 52 | "execution_count": null, 53 | "outputs": [] 54 | }, 55 | { 56 | "cell_type": "markdown", 57 | "id": "y3agbw37a7", 58 | "source": "## Analysis", 59 | "metadata": {} 60 | }, 61 | { 62 | "cell_type": "markdown", 63 | "id": "i9gnnpy7jc8", 64 | "source": "### HeartDisease (Response Variable)", 65 | "metadata": {} 66 | }, 67 | { 68 | "cell_type": "code", 69 | "id": "epucxsz6f1c", 70 | "source": "# HeartDisease: Binary variable\nfreq_counts = df['HeartDisease'].value_counts().sort_index()\nrel_freq = df['HeartDisease'].value_counts(normalize=True).sort_index()\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(8, 5))\nfreq_counts.plot(kind='bar', color=['skyblue', 'salmon'])\nplt.title('Distribution of HeartDisease')\nplt.xlabel('HeartDisease (0 = No, 1 = Yes)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=0)\nplt.tight_layout()\nplt.show()", 71 | "metadata": {}, 72 | "execution_count": null, 73 | "outputs": [] 74 | }, 75 | { 76 | "cell_type": "markdown", 77 | "id": "82c8hzinx2o", 78 | "source": "The **HeartDisease** variable is a binary categorical variable that serves as the response variable in this dataset. It encodes the presence (1) or absence (0) of heart disease in subjects. The distribution shows that the majority of subjects in the dataset have heart disease.", 79 | "metadata": {} 80 | }, 81 | { 82 | "cell_type": "markdown", 83 | "id": "z85d8lcblie", 84 | "source": "### Age", 85 | "metadata": {} 86 | }, 87 | { 88 | "cell_type": "code", 89 | "id": "xvvwvkpchq", 90 | "source": "# Age: Numerical variable\nage_mean = df['Age'].mean()\nage_std = df['Age'].std()\nage_var = df['Age'].var()\nage_min = df['Age'].min()\nage_max = df['Age'].max()\nage_q1 = df['Age'].quantile(0.25)\nage_q3 = df['Age'].quantile(0.75)\nage_iqr = age_q3 - age_q1\n\nprint(f\"Sample Mean: {age_mean:.2f} years\")\nprint(f\"Standard Deviation: {age_std:.2f} years\")\nprint(f\"Variance: {age_var:.2f}\")\nprint(f\"Minimum: {age_min} years\")\nprint(f\"Maximum: {age_max} years\")\nprint(f\"Q1 (25th percentile): {age_q1} years\")\nprint(f\"Q3 (75th percentile): {age_q3} years\")\nprint(f\"Interquartile Range (IQR): {age_iqr} years\")\n\n# Box-and-whisker plot\nplt.figure(figsize=(8, 5))\nplt.boxplot(df['Age'], vert=False)\nplt.title('Distribution of Age')\nplt.xlabel('Age (years)')\nplt.tight_layout()\nplt.show()", 91 | "metadata": {}, 92 | "execution_count": null, 93 | "outputs": [] 94 | }, 95 | { 96 | "cell_type": "markdown", 97 | "id": "pavylnv41m", 98 | "source": "The **Age** variable is a numerical variable representing the age of subjects measured in years. The sample shows a mean age of approximately 54 years with a standard deviation of about 9 years, indicating moderate variability in age across the dataset.", 99 | "metadata": {} 100 | }, 101 | { 102 | "cell_type": "markdown", 103 | "id": "s28q5lpstd", 104 | "source": "### Sex", 105 | "metadata": {} 106 | }, 107 | { 108 | "cell_type": "code", 109 | "id": "6xh858j3ama", 110 | "source": "# Sex: Binary categorical variable\nfreq_counts = df['Sex'].value_counts()\nrel_freq = df['Sex'].value_counts(normalize=True)\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(8, 5))\nfreq_counts.plot(kind='bar', color=['lightblue', 'pink'])\nplt.title('Distribution of Sex')\nplt.xlabel('Sex (M = Male, F = Female)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=0)\nplt.tight_layout()\nplt.show()", 111 | "metadata": {}, 112 | "execution_count": null, 113 | "outputs": [] 114 | }, 115 | { 116 | "cell_type": "markdown", 117 | "id": "wsyv6h2mfag", 118 | "source": "The **Sex** variable is a binary categorical variable where \"M\" encodes male subjects and \"F\" encodes female subjects. The dataset contains significantly more male subjects than female subjects.", 119 | "metadata": {} 120 | }, 121 | { 122 | "cell_type": "markdown", 123 | "id": "rzha4aeosn", 124 | "source": "### ChestPainType", 125 | "metadata": {} 126 | }, 127 | { 128 | "cell_type": "code", 129 | "id": "m3tju2zpbvm", 130 | "source": "# ChestPainType: Nominal categorical variable\nfreq_counts = df['ChestPainType'].value_counts()\nrel_freq = df['ChestPainType'].value_counts(normalize=True)\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(10, 5))\nfreq_counts.plot(kind='bar', color='steelblue')\nplt.title('Distribution of Chest Pain Type')\nplt.xlabel('Chest Pain Type (ATA = Atypical Angina, ASY = Asymptomatic, NAP = Non-Anginal Pain, TA = Typical Angina)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()", 131 | "metadata": {}, 132 | "execution_count": null, 133 | "outputs": [] 134 | }, 135 | { 136 | "cell_type": "markdown", 137 | "id": "qcvpleptual", 138 | "source": "The **ChestPainType** variable is a nominal categorical variable with four categories: \"ATA\" (atypical angina), \"ASY\" (asymptomatic), \"NAP\" (non-anginal pain), and \"TA\" (typical angina). The asymptomatic category is the most common in this dataset.", 139 | "metadata": {} 140 | }, 141 | { 142 | "cell_type": "markdown", 143 | "id": "33ghmg3cwob", 144 | "source": "### RestingBP", 145 | "metadata": {} 146 | }, 147 | { 148 | "cell_type": "code", 149 | "id": "1xeqpndyfmej", 150 | "source": "# RestingBP: Numerical variable\nbp_mean = df['RestingBP'].mean()\nbp_std = df['RestingBP'].std()\nbp_var = df['RestingBP'].var()\nbp_min = df['RestingBP'].min()\nbp_max = df['RestingBP'].max()\nbp_q1 = df['RestingBP'].quantile(0.25)\nbp_q3 = df['RestingBP'].quantile(0.75)\nbp_iqr = bp_q3 - bp_q1\n\nprint(f\"Sample Mean: {bp_mean:.2f} mm Hg\")\nprint(f\"Standard Deviation: {bp_std:.2f} mm Hg\")\nprint(f\"Variance: {bp_var:.2f}\")\nprint(f\"Minimum: {bp_min} mm Hg\")\nprint(f\"Maximum: {bp_max} mm Hg\")\nprint(f\"Q1 (25th percentile): {bp_q1} mm Hg\")\nprint(f\"Q3 (75th percentile): {bp_q3} mm Hg\")\nprint(f\"Interquartile Range (IQR): {bp_iqr} mm Hg\")\n\n# Box-and-whisker plot\nplt.figure(figsize=(8, 5))\nplt.boxplot(df['RestingBP'], vert=False)\nplt.title('Distribution of Resting Blood Pressure')\nplt.xlabel('Resting BP (mm Hg)')\nplt.tight_layout()\nplt.show()", 151 | "metadata": {}, 152 | "execution_count": null, 153 | "outputs": [] 154 | }, 155 | { 156 | "cell_type": "markdown", 157 | "id": "a2hvhgeuwpq", 158 | "source": "The **RestingBP** variable is a numerical variable representing the resting systolic blood pressure of subjects measured in mm Hg. The dataset shows a mean resting blood pressure of approximately 132 mm Hg with moderate variability.", 159 | "metadata": {} 160 | }, 161 | { 162 | "cell_type": "markdown", 163 | "id": "3vm49jzoh9v", 164 | "source": "### Cholesterol", 165 | "metadata": {} 166 | }, 167 | { 168 | "cell_type": "code", 169 | "id": "8imm1wa7hog", 170 | "source": "# Cholesterol: Numerical variable\nchol_mean = df['Cholesterol'].mean()\nchol_std = df['Cholesterol'].std()\nchol_var = df['Cholesterol'].var()\nchol_min = df['Cholesterol'].min()\nchol_max = df['Cholesterol'].max()\nchol_q1 = df['Cholesterol'].quantile(0.25)\nchol_q3 = df['Cholesterol'].quantile(0.75)\nchol_iqr = chol_q3 - chol_q1\n\nprint(f\"Sample Mean: {chol_mean:.2f} mg/dL\")\nprint(f\"Standard Deviation: {chol_std:.2f} mg/dL\")\nprint(f\"Variance: {chol_var:.2f}\")\nprint(f\"Minimum: {chol_min} mg/dL\")\nprint(f\"Maximum: {chol_max} mg/dL\")\nprint(f\"Q1 (25th percentile): {chol_q1} mg/dL\")\nprint(f\"Q3 (75th percentile): {chol_q3} mg/dL\")\nprint(f\"Interquartile Range (IQR): {chol_iqr} mg/dL\")\n\n# Box-and-whisker plot\nplt.figure(figsize=(8, 5))\nplt.boxplot(df['Cholesterol'], vert=False)\nplt.title('Distribution of Cholesterol')\nplt.xlabel('Cholesterol (mg/dL)')\nplt.tight_layout()\nplt.show()", 171 | "metadata": {}, 172 | "execution_count": null, 173 | "outputs": [] 174 | }, 175 | { 176 | "cell_type": "markdown", 177 | "id": "dkajtf6y7u", 178 | "source": "The **Cholesterol** variable is a numerical variable representing serum total cholesterol measured in mg/dL. Notably, there are many zero values in this dataset, which likely represent missing or unmeasured data rather than actual cholesterol readings of zero.", 179 | "metadata": {} 180 | }, 181 | { 182 | "cell_type": "markdown", 183 | "id": "2jvk064tnol", 184 | "source": "### FastingBS", 185 | "metadata": {} 186 | }, 187 | { 188 | "cell_type": "code", 189 | "id": "tmxau4wsx1d", 190 | "source": "# FastingBS: Binary variable\nfreq_counts = df['FastingBS'].value_counts().sort_index()\nrel_freq = df['FastingBS'].value_counts(normalize=True).sort_index()\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(8, 5))\nfreq_counts.plot(kind='bar', color=['lightgreen', 'coral'])\nplt.title('Distribution of Fasting Blood Sugar')\nplt.xlabel('FastingBS (0 = ≤120 mg/dL, 1 = >120 mg/dL)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=0)\nplt.tight_layout()\nplt.show()", 191 | "metadata": {}, 192 | "execution_count": null, 193 | "outputs": [] 194 | }, 195 | { 196 | "cell_type": "markdown", 197 | "id": "zcmjfmqmgnd", 198 | "source": "The **FastingBS** variable is a binary variable indicating fasting blood sugar levels, where 0 represents fasting blood sugar ≤120 mg/dL and 1 represents fasting blood sugar >120 mg/dL. The majority of subjects have normal fasting blood sugar levels.", 199 | "metadata": {} 200 | }, 201 | { 202 | "cell_type": "markdown", 203 | "id": "6h3dtir532u", 204 | "source": "### RestingECG", 205 | "metadata": {} 206 | }, 207 | { 208 | "cell_type": "code", 209 | "id": "t1zmtnj81h", 210 | "source": "# RestingECG: Categorical variable\nfreq_counts = df['RestingECG'].value_counts()\nrel_freq = df['RestingECG'].value_counts(normalize=True)\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(10, 5))\nfreq_counts.plot(kind='bar', color='mediumpurple')\nplt.title('Distribution of Resting ECG')\nplt.xlabel('RestingECG (Normal, ST, LVH)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()", 211 | "metadata": {}, 212 | "execution_count": null, 213 | "outputs": [] 214 | }, 215 | { 216 | "cell_type": "markdown", 217 | "id": "xjk1ud9gi0p", 218 | "source": "The **RestingECG** variable is a categorical variable representing resting electrocardiogram results with three categories: \"Normal\" (normal ECG), \"ST\" (ST-segment changes), and \"LVH\" (left ventricular hypertrophy). The majority of subjects show normal ECG results.", 219 | "metadata": {} 220 | }, 221 | { 222 | "cell_type": "markdown", 223 | "id": "22sanp6nepg", 224 | "source": "### MaxHR", 225 | "metadata": {} 226 | }, 227 | { 228 | "cell_type": "code", 229 | "id": "2ya3j8yv6x4", 230 | "source": "# MaxHR: Numerical variable\nhr_mean = df['MaxHR'].mean()\nhr_std = df['MaxHR'].std()\nhr_var = df['MaxHR'].var()\nhr_min = df['MaxHR'].min()\nhr_max = df['MaxHR'].max()\nhr_q1 = df['MaxHR'].quantile(0.25)\nhr_q3 = df['MaxHR'].quantile(0.75)\nhr_iqr = hr_q3 - hr_q1\n\nprint(f\"Sample Mean: {hr_mean:.2f} bpm\")\nprint(f\"Standard Deviation: {hr_std:.2f} bpm\")\nprint(f\"Variance: {hr_var:.2f}\")\nprint(f\"Minimum: {hr_min} bpm\")\nprint(f\"Maximum: {hr_max} bpm\")\nprint(f\"Q1 (25th percentile): {hr_q1} bpm\")\nprint(f\"Q3 (75th percentile): {hr_q3} bpm\")\nprint(f\"Interquartile Range (IQR): {hr_iqr} bpm\")\n\n# Box-and-whisker plot\nplt.figure(figsize=(8, 5))\nplt.boxplot(df['MaxHR'], vert=False)\nplt.title('Distribution of Maximum Heart Rate')\nplt.xlabel('MaxHR (bpm)')\nplt.tight_layout()\nplt.show()", 231 | "metadata": {}, 232 | "execution_count": null, 233 | "outputs": [] 234 | }, 235 | { 236 | "cell_type": "markdown", 237 | "id": "4hil1ls2312", 238 | "source": "The **MaxHR** variable is a numerical variable representing the maximum heart rate achieved by subjects measured in beats per minute (bpm). The sample shows a mean maximum heart rate of approximately 137 bpm with considerable variability across subjects.", 239 | "metadata": {} 240 | }, 241 | { 242 | "cell_type": "markdown", 243 | "id": "56ee37rw48", 244 | "source": "### ExerciseAngina", 245 | "metadata": {} 246 | }, 247 | { 248 | "cell_type": "code", 249 | "id": "ihno2dn3syr", 250 | "source": "# ExerciseAngina: Binary categorical variable\nfreq_counts = df['ExerciseAngina'].value_counts()\nrel_freq = df['ExerciseAngina'].value_counts(normalize=True)\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(8, 5))\nfreq_counts.plot(kind='bar', color=['lightgreen', 'salmon'])\nplt.title('Distribution of Exercise-Induced Angina')\nplt.xlabel('ExerciseAngina (N = No, Y = Yes)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=0)\nplt.tight_layout()\nplt.show()", 251 | "metadata": {}, 252 | "execution_count": null, 253 | "outputs": [] 254 | }, 255 | { 256 | "cell_type": "markdown", 257 | "id": "nphkpdxwo9b", 258 | "source": "The **ExerciseAngina** variable is a binary categorical variable where \"N\" encodes the absence of exercise-induced angina and \"Y\" encodes the presence of exercise-induced angina. The majority of subjects do not experience exercise-induced angina.", 259 | "metadata": {} 260 | }, 261 | { 262 | "cell_type": "markdown", 263 | "id": "44vevrbu51k", 264 | "source": "### ST_Slope", 265 | "metadata": {} 266 | }, 267 | { 268 | "cell_type": "code", 269 | "id": "j1k93luk9se", 270 | "source": "# ST_Slope: Categorical variable\nfreq_counts = df['ST_Slope'].value_counts()\nrel_freq = df['ST_Slope'].value_counts(normalize=True)\n\nprint(\"Frequency Counts:\")\nprint(freq_counts)\nprint(\"\\nRelative Frequency:\")\nprint(rel_freq)\n\n# Bar plot\nplt.figure(figsize=(10, 5))\nfreq_counts.plot(kind='bar', color='teal')\nplt.title('Distribution of ST Slope')\nplt.xlabel('ST_Slope (Flat, Up, Down)')\nplt.ylabel('Frequency')\nplt.xticks(rotation=45)\nplt.tight_layout()\nplt.show()", 271 | "metadata": {}, 272 | "execution_count": null, 273 | "outputs": [] 274 | }, 275 | { 276 | "cell_type": "markdown", 277 | "id": "lygs5swimae", 278 | "source": "The **ST_Slope** variable is a categorical variable representing the slope of the ST segment on an ECG with three categories: \"Flat\" (normal), \"Up\" (ST-segment elevation), and \"Down\" (ST-segment depression). The \"Flat\" category is the most common in this dataset.", 279 | "metadata": {} 280 | }, 281 | { 282 | "cell_type": "markdown", 283 | "id": "f7xnjcmq13q", 284 | "source": "## Conclusion", 285 | "metadata": {} 286 | }, 287 | { 288 | "cell_type": "markdown", 289 | "id": "la8lyqxvguk", 290 | "source": "This heart disease dataset contains 918 observations with 11 variables, including both numerical and categorical features. Key characteristics of the dataset include:\n\n**Demographics and Baseline Characteristics:**\n- The subjects have a mean age of approximately 54 years with moderate variability\n- The dataset is heavily skewed toward male subjects, with significantly more males than females\n- Most subjects show normal resting ECG results and do not experience exercise-induced angina\n\n**Cardiovascular Measurements:**\n- Mean resting blood pressure is approximately 132 mm Hg\n- The Cholesterol variable contains many zero values, likely representing missing data\n- Mean maximum heart rate is approximately 137 bpm with considerable variability\n- Most subjects have normal fasting blood sugar levels (≤120 mg/dL)\n\n**Clinical Indicators:**\n- The majority of subjects are asymptomatic in terms of chest pain type\n- The ST_Slope distribution shows \"Flat\" as the most common category\n- Most subjects do not experience exercise-induced angina\n\n**Response Variable:**\n- The HeartDisease variable shows that the majority of subjects in this dataset have heart disease (encoded as 1)\n\n**Data Quality Considerations:**\n- The Cholesterol variable contains zero values that likely represent missing data and should be handled appropriately in any predictive modeling\n- The dataset is imbalanced in terms of sex distribution, which may need to be considered in subsequent analyses\n\nThis comprehensive dataset provides a rich collection of features for studying heart disease risk factors and could be valuable for developing predictive models for heart disease diagnosis.", 291 | "metadata": {} 292 | } 293 | ], 294 | "metadata": { 295 | "kernelspec": { 296 | "display_name": "Python 3", 297 | "language": "python", 298 | "name": "python3" 299 | }, 300 | "language_info": { 301 | "codemirror_mode": { 302 | "name": "ipython", 303 | "version": 3 304 | }, 305 | "file_extension": ".py", 306 | "mimetype": "text/x-python", 307 | "name": "python", 308 | "nbconvert_exporter": "python", 309 | "pygments_lexer": "ipython3", 310 | "version": "3.8.0" 311 | } 312 | }, 313 | "nbformat": 4, 314 | "nbformat_minor": 5 315 | } -------------------------------------------------------------------------------- /heart.csv: -------------------------------------------------------------------------------- 1 | Age,Sex,ChestPainType,RestingBP,Cholesterol,FastingBS,RestingECG,MaxHR,ExerciseAngina,ST_Slope,HeartDisease 2 | 40,M,ATA,140,289,0,Normal,172,N,Up,0 3 | 49,F,NAP,160,180,0,Normal,156,N,Flat,1 4 | 37,M,ATA,130,283,0,ST,98,N,Up,0 5 | 48,F,ASY,138,214,0,Normal,108,Y,Flat,1 6 | 54,M,NAP,150,195,0,Normal,122,N,Up,0 7 | 39,M,NAP,120,339,0,Normal,170,N,Up,0 8 | 45,F,ATA,130,237,0,Normal,170,N,Up,0 9 | 54,M,ATA,110,208,0,Normal,142,N,Up,0 10 | 37,M,ASY,140,207,0,Normal,130,Y,Flat,1 11 | 48,F,ATA,120,284,0,Normal,120,N,Up,0 12 | 37,F,NAP,130,211,0,Normal,142,N,Up,0 13 | 58,M,ATA,136,164,0,ST,99,Y,Flat,1 14 | 39,M,ATA,120,204,0,Normal,145,N,Up,0 15 | 49,M,ASY,140,234,0,Normal,140,Y,Flat,1 16 | 42,F,NAP,115,211,0,ST,137,N,Up,0 17 | 54,F,ATA,120,273,0,Normal,150,N,Flat,0 18 | 38,M,ASY,110,196,0,Normal,166,N,Flat,1 19 | 43,F,ATA,120,201,0,Normal,165,N,Up,0 20 | 60,M,ASY,100,248,0,Normal,125,N,Flat,1 21 | 36,M,ATA,120,267,0,Normal,160,N,Flat,1 22 | 43,F,TA,100,223,0,Normal,142,N,Up,0 23 | 44,M,ATA,120,184,0,Normal,142,N,Flat,0 24 | 49,F,ATA,124,201,0,Normal,164,N,Up,0 25 | 44,M,ATA,150,288,0,Normal,150,Y,Flat,1 26 | 40,M,NAP,130,215,0,Normal,138,N,Up,0 27 | 36,M,NAP,130,209,0,Normal,178,N,Up,0 28 | 53,M,ASY,124,260,0,ST,112,Y,Flat,0 29 | 52,M,ATA,120,284,0,Normal,118,N,Up,0 30 | 53,F,ATA,113,468,0,Normal,127,N,Up,0 31 | 51,M,ATA,125,188,0,Normal,145,N,Up,0 32 | 53,M,NAP,145,518,0,Normal,130,N,Flat,1 33 | 56,M,NAP,130,167,0,Normal,114,N,Up,0 34 | 54,M,ASY,125,224,0,Normal,122,N,Flat,1 35 | 41,M,ASY,130,172,0,ST,130,N,Flat,1 36 | 43,F,ATA,150,186,0,Normal,154,N,Up,0 37 | 32,M,ATA,125,254,0,Normal,155,N,Up,0 38 | 65,M,ASY,140,306,1,Normal,87,Y,Flat,1 39 | 41,F,ATA,110,250,0,ST,142,N,Up,0 40 | 48,F,ATA,120,177,1,ST,148,N,Up,0 41 | 48,F,ASY,150,227,0,Normal,130,Y,Flat,0 42 | 54,F,ATA,150,230,0,Normal,130,N,Up,0 43 | 54,F,NAP,130,294,0,ST,100,Y,Flat,1 44 | 35,M,ATA,150,264,0,Normal,168,N,Up,0 45 | 52,M,NAP,140,259,0,ST,170,N,Up,0 46 | 43,M,ASY,120,175,0,Normal,120,Y,Flat,1 47 | 59,M,NAP,130,318,0,Normal,120,Y,Flat,0 48 | 37,M,ASY,120,223,0,Normal,168,N,Up,0 49 | 50,M,ATA,140,216,0,Normal,170,N,Up,0 50 | 36,M,NAP,112,340,0,Normal,184,N,Flat,0 51 | 41,M,ASY,110,289,0,Normal,170,N,Flat,1 52 | 50,M,ASY,130,233,0,Normal,121,Y,Flat,1 53 | 47,F,ASY,120,205,0,Normal,98,Y,Flat,1 54 | 45,M,ATA,140,224,1,Normal,122,N,Up,0 55 | 41,F,ATA,130,245,0,Normal,150,N,Up,0 56 | 52,F,ASY,130,180,0,Normal,140,Y,Flat,0 57 | 51,F,ATA,160,194,0,Normal,170,N,Up,0 58 | 31,M,ASY,120,270,0,Normal,153,Y,Flat,1 59 | 58,M,NAP,130,213,0,ST,140,N,Flat,1 60 | 54,M,ASY,150,365,0,ST,134,N,Up,0 61 | 52,M,ASY,112,342,0,ST,96,Y,Flat,1 62 | 49,M,ATA,100,253,0,Normal,174,N,Up,0 63 | 43,F,NAP,150,254,0,Normal,175,N,Up,0 64 | 45,M,ASY,140,224,0,Normal,144,N,Up,0 65 | 46,M,ASY,120,277,0,Normal,125,Y,Flat,1 66 | 50,F,ATA,110,202,0,Normal,145,N,Up,0 67 | 37,F,ATA,120,260,0,Normal,130,N,Up,0 68 | 45,F,ASY,132,297,0,Normal,144,N,Up,0 69 | 32,M,ATA,110,225,0,Normal,184,N,Up,0 70 | 52,M,ASY,160,246,0,ST,82,Y,Flat,1 71 | 44,M,ASY,150,412,0,Normal,170,N,Up,0 72 | 57,M,ATA,140,265,0,ST,145,Y,Flat,1 73 | 44,M,ATA,130,215,0,Normal,135,N,Up,0 74 | 52,M,ASY,120,182,0,Normal,150,N,Flat,1 75 | 44,F,ASY,120,218,0,ST,115,N,Up,0 76 | 55,M,ASY,140,268,0,Normal,128,Y,Flat,1 77 | 46,M,NAP,150,163,0,Normal,116,N,Up,0 78 | 32,M,ASY,118,529,0,Normal,130,N,Flat,1 79 | 35,F,ASY,140,167,0,Normal,150,N,Up,0 80 | 52,M,ATA,140,100,0,Normal,138,Y,Up,0 81 | 49,M,ASY,130,206,0,Normal,170,N,Flat,1 82 | 55,M,NAP,110,277,0,Normal,160,N,Up,0 83 | 54,M,ATA,120,238,0,Normal,154,N,Up,0 84 | 63,M,ASY,150,223,0,Normal,115,N,Flat,1 85 | 52,M,ATA,160,196,0,Normal,165,N,Up,0 86 | 56,M,ASY,150,213,1,Normal,125,Y,Flat,1 87 | 66,M,ASY,140,139,0,Normal,94,Y,Flat,1 88 | 65,M,ASY,170,263,1,Normal,112,Y,Flat,1 89 | 53,F,ATA,140,216,0,Normal,142,Y,Flat,0 90 | 43,M,TA,120,291,0,ST,155,N,Flat,1 91 | 55,M,ASY,140,229,0,Normal,110,Y,Flat,0 92 | 49,F,ATA,110,208,0,Normal,160,N,Up,0 93 | 39,M,ASY,130,307,0,Normal,140,N,Up,0 94 | 52,F,ATA,120,210,0,Normal,148,N,Up,0 95 | 48,M,ASY,160,329,0,Normal,92,Y,Flat,1 96 | 39,F,NAP,110,182,0,ST,180,N,Up,0 97 | 58,M,ASY,130,263,0,Normal,140,Y,Flat,1 98 | 43,M,ATA,142,207,0,Normal,138,N,Up,0 99 | 39,M,NAP,160,147,1,Normal,160,N,Up,0 100 | 56,M,ASY,120,85,0,Normal,140,N,Up,0 101 | 41,M,ATA,125,269,0,Normal,144,N,Up,0 102 | 65,M,ASY,130,275,0,ST,115,Y,Flat,1 103 | 51,M,ASY,130,179,0,Normal,100,N,Up,0 104 | 40,F,ASY,150,392,0,Normal,130,N,Flat,1 105 | 40,M,ASY,120,466,1,Normal,152,Y,Flat,1 106 | 46,M,ASY,118,186,0,Normal,124,N,Flat,1 107 | 57,M,ATA,140,260,1,Normal,140,N,Up,0 108 | 48,F,ASY,120,254,0,ST,110,N,Up,0 109 | 34,M,ATA,150,214,0,ST,168,N,Up,0 110 | 50,M,ASY,140,129,0,Normal,135,N,Up,0 111 | 39,M,ATA,190,241,0,Normal,106,N,Up,0 112 | 59,F,ATA,130,188,0,Normal,124,N,Flat,0 113 | 57,M,ASY,150,255,0,Normal,92,Y,Flat,1 114 | 47,M,ASY,140,276,1,Normal,125,Y,Up,0 115 | 38,M,ATA,140,297,0,Normal,150,N,Up,0 116 | 49,F,NAP,130,207,0,ST,135,N,Up,0 117 | 33,F,ASY,100,246,0,Normal,150,Y,Flat,1 118 | 38,M,ASY,120,282,0,Normal,170,N,Flat,1 119 | 59,F,ASY,130,338,1,ST,130,Y,Flat,1 120 | 35,F,TA,120,160,0,ST,185,N,Up,0 121 | 34,M,TA,140,156,0,Normal,180,N,Flat,1 122 | 47,F,NAP,135,248,1,Normal,170,N,Flat,1 123 | 52,F,NAP,125,272,0,Normal,139,N,Up,0 124 | 46,M,ASY,110,240,0,ST,140,N,Up,0 125 | 58,F,ATA,180,393,0,Normal,110,Y,Flat,1 126 | 58,M,ATA,130,230,0,Normal,150,N,Up,0 127 | 54,M,ATA,120,246,0,Normal,110,N,Up,0 128 | 34,F,ATA,130,161,0,Normal,190,N,Up,0 129 | 48,F,ASY,108,163,0,Normal,175,N,Up,0 130 | 54,F,ATA,120,230,1,Normal,140,N,Up,0 131 | 42,M,NAP,120,228,0,Normal,152,Y,Flat,0 132 | 38,M,NAP,145,292,0,Normal,130,N,Up,0 133 | 46,M,ASY,110,202,0,Normal,150,Y,Flat,1 134 | 56,M,ASY,170,388,0,ST,122,Y,Flat,1 135 | 56,M,ASY,150,230,0,ST,124,Y,Flat,1 136 | 61,F,ASY,130,294,0,ST,120,Y,Flat,0 137 | 49,M,NAP,115,265,0,Normal,175,N,Flat,1 138 | 43,F,ATA,120,215,0,ST,175,N,Up,0 139 | 39,M,ATA,120,241,0,ST,146,N,Up,0 140 | 54,M,ASY,140,166,0,Normal,118,Y,Flat,1 141 | 43,M,ASY,150,247,0,Normal,130,Y,Flat,1 142 | 52,M,ASY,160,331,0,Normal,94,Y,Flat,1 143 | 50,M,ASY,140,341,0,ST,125,Y,Flat,1 144 | 47,M,ASY,160,291,0,ST,158,Y,Flat,1 145 | 53,M,ASY,140,243,0,Normal,155,N,Up,0 146 | 56,F,ATA,120,279,0,Normal,150,N,Flat,1 147 | 39,M,ASY,110,273,0,Normal,132,N,Up,0 148 | 42,M,ATA,120,198,0,Normal,155,N,Up,0 149 | 43,F,ATA,120,249,0,ST,176,N,Up,0 150 | 50,M,ATA,120,168,0,Normal,160,N,Up,0 151 | 54,M,ASY,130,603,1,Normal,125,Y,Flat,1 152 | 39,M,ATA,130,215,0,Normal,120,N,Up,0 153 | 48,M,ATA,100,159,0,Normal,100,N,Up,0 154 | 40,M,ATA,130,275,0,Normal,150,N,Up,0 155 | 55,M,ASY,120,270,0,Normal,140,N,Up,0 156 | 41,M,ATA,120,291,0,ST,160,N,Up,0 157 | 56,M,ASY,155,342,1,Normal,150,Y,Flat,1 158 | 38,M,ASY,110,190,0,Normal,150,Y,Flat,1 159 | 49,M,ASY,140,185,0,Normal,130,N,Up,0 160 | 44,M,ASY,130,290,0,Normal,100,Y,Flat,1 161 | 54,M,ATA,160,195,0,ST,130,N,Up,0 162 | 59,M,ASY,140,264,1,LVH,119,Y,Flat,1 163 | 49,M,ASY,128,212,0,Normal,96,Y,Flat,1 164 | 47,M,ATA,160,263,0,Normal,174,N,Up,0 165 | 42,M,ATA,120,196,0,Normal,150,N,Up,0 166 | 52,F,ATA,140,225,0,Normal,140,N,Up,0 167 | 46,M,TA,140,272,1,Normal,175,N,Flat,1 168 | 50,M,ASY,140,231,0,ST,140,Y,Flat,1 169 | 48,M,ATA,140,238,0,Normal,118,N,Up,0 170 | 58,M,ASY,135,222,0,Normal,100,N,Up,0 171 | 58,M,NAP,140,179,0,Normal,160,N,Up,0 172 | 29,M,ATA,120,243,0,Normal,160,N,Up,0 173 | 40,M,NAP,140,235,0,Normal,188,N,Up,0 174 | 53,M,ATA,140,320,0,Normal,162,N,Up,0 175 | 49,M,NAP,140,187,0,Normal,172,N,Up,0 176 | 52,M,ASY,140,266,0,Normal,134,Y,Flat,1 177 | 43,M,ASY,140,288,0,Normal,135,Y,Flat,1 178 | 54,M,ASY,140,216,0,Normal,105,N,Flat,1 179 | 59,M,ATA,140,287,0,Normal,150,N,Up,0 180 | 37,M,NAP,130,194,0,Normal,150,N,Up,0 181 | 46,F,ASY,130,238,0,Normal,90,N,Up,0 182 | 52,M,ASY,130,225,0,Normal,120,Y,Flat,1 183 | 51,M,ATA,130,224,0,Normal,150,N,Up,0 184 | 52,M,ASY,140,404,0,Normal,124,Y,Flat,1 185 | 46,M,ASY,110,238,0,ST,140,Y,Flat,0 186 | 54,F,ATA,160,312,0,Normal,130,N,Up,0 187 | 58,M,NAP,160,211,1,ST,92,N,Flat,1 188 | 58,M,ATA,130,251,0,Normal,110,N,Up,0 189 | 41,M,ASY,120,237,1,Normal,138,Y,Flat,1 190 | 50,F,ASY,120,328,0,Normal,110,Y,Flat,0 191 | 53,M,ASY,180,285,0,ST,120,Y,Flat,1 192 | 46,M,ASY,180,280,0,ST,120,N,Up,0 193 | 50,M,ATA,170,209,0,ST,116,N,Up,0 194 | 48,M,ATA,130,245,0,Normal,160,N,Up,0 195 | 45,M,NAP,135,192,0,Normal,110,N,Up,0 196 | 41,F,ATA,125,184,0,Normal,180,N,Up,0 197 | 62,F,TA,160,193,0,Normal,116,N,Up,0 198 | 49,M,ASY,120,297,0,Normal,132,N,Flat,0 199 | 42,M,ATA,150,268,0,Normal,136,N,Up,0 200 | 53,M,ASY,120,246,0,Normal,116,Y,Flat,1 201 | 57,F,TA,130,308,0,Normal,98,N,Flat,0 202 | 47,M,TA,110,249,0,Normal,150,N,Up,0 203 | 46,M,NAP,120,230,0,Normal,150,N,Up,0 204 | 42,M,NAP,160,147,0,Normal,146,N,Up,0 205 | 31,F,ATA,100,219,0,ST,150,N,Up,0 206 | 56,M,ATA,130,184,0,Normal,100,N,Up,0 207 | 50,M,ASY,150,215,0,Normal,140,Y,Up,0 208 | 35,M,ATA,120,308,0,LVH,180,N,Up,0 209 | 35,M,ATA,110,257,0,Normal,140,N,Flat,1 210 | 28,M,ATA,130,132,0,LVH,185,N,Up,0 211 | 54,M,ASY,125,216,0,Normal,140,N,Flat,1 212 | 48,M,ASY,106,263,1,Normal,110,N,Flat,1 213 | 50,F,NAP,140,288,0,Normal,140,Y,Flat,1 214 | 56,M,NAP,130,276,0,Normal,128,Y,Up,0 215 | 56,F,NAP,130,219,0,ST,164,N,Up,0 216 | 47,M,ASY,150,226,0,Normal,98,Y,Flat,1 217 | 30,F,TA,170,237,0,ST,170,N,Up,0 218 | 39,M,ASY,110,280,0,Normal,150,N,Flat,1 219 | 54,M,NAP,120,217,0,Normal,137,N,Up,0 220 | 55,M,ATA,140,196,0,Normal,150,N,Up,0 221 | 29,M,ATA,140,263,0,Normal,170,N,Up,0 222 | 46,M,ASY,130,222,0,Normal,112,N,Flat,1 223 | 51,F,ASY,160,303,0,Normal,150,Y,Flat,1 224 | 48,F,NAP,120,195,0,Normal,125,N,Up,0 225 | 33,M,NAP,120,298,0,Normal,185,N,Up,0 226 | 55,M,ATA,120,256,1,Normal,137,N,Up,0 227 | 50,M,ASY,145,264,0,Normal,150,N,Flat,1 228 | 53,M,NAP,120,195,0,Normal,140,N,Up,0 229 | 38,M,ASY,92,117,0,Normal,134,Y,Flat,1 230 | 41,M,ATA,120,295,0,Normal,170,N,Up,0 231 | 37,F,ASY,130,173,0,ST,184,N,Up,0 232 | 37,M,ASY,130,315,0,Normal,158,N,Up,0 233 | 40,M,NAP,130,281,0,Normal,167,N,Up,0 234 | 38,F,ATA,120,275,0,Normal,129,N,Up,0 235 | 41,M,ASY,112,250,0,Normal,142,N,Up,0 236 | 54,F,ATA,140,309,0,ST,140,N,Up,0 237 | 39,M,ATA,120,200,0,Normal,160,Y,Flat,0 238 | 41,M,ASY,120,336,0,Normal,118,Y,Flat,1 239 | 55,M,TA,140,295,0,Normal,136,N,Flat,1 240 | 48,M,ASY,160,355,0,Normal,99,Y,Flat,1 241 | 48,M,ASY,160,193,0,Normal,102,Y,Flat,1 242 | 55,M,ATA,145,326,0,Normal,155,N,Up,0 243 | 54,M,ASY,200,198,0,Normal,142,Y,Flat,1 244 | 55,M,ATA,160,292,1,Normal,143,Y,Flat,1 245 | 43,F,ATA,120,266,0,Normal,118,N,Up,0 246 | 48,M,ASY,160,268,0,Normal,103,Y,Flat,1 247 | 54,M,TA,120,171,0,Normal,137,N,Up,0 248 | 54,M,NAP,120,237,0,Normal,150,Y,Flat,1 249 | 48,M,ASY,122,275,1,ST,150,Y,Down,1 250 | 45,M,ASY,130,219,0,ST,130,Y,Flat,1 251 | 49,M,ASY,130,341,0,Normal,120,Y,Flat,1 252 | 44,M,ASY,135,491,0,Normal,135,N,Flat,1 253 | 48,M,ASY,120,260,0,Normal,115,N,Flat,1 254 | 61,M,ASY,125,292,0,ST,115,Y,Up,0 255 | 62,M,ATA,140,271,0,Normal,152,N,Up,0 256 | 55,M,ASY,145,248,0,Normal,96,Y,Flat,1 257 | 53,F,NAP,120,274,0,Normal,130,N,Up,0 258 | 55,F,ATA,130,394,0,LVH,150,N,Up,0 259 | 36,M,NAP,150,160,0,Normal,172,N,Up,0 260 | 51,F,NAP,150,200,0,Normal,120,N,Up,0 261 | 55,F,ATA,122,320,0,Normal,155,N,Up,0 262 | 46,M,ATA,140,275,0,Normal,165,Y,Up,0 263 | 54,F,ATA,120,221,0,Normal,138,N,Up,0 264 | 46,M,ASY,120,231,0,Normal,115,Y,Flat,1 265 | 59,M,ASY,130,126,0,Normal,125,N,Flat,1 266 | 47,M,NAP,140,193,0,Normal,145,Y,Flat,1 267 | 54,M,ATA,160,305,0,Normal,175,N,Up,0 268 | 52,M,ASY,130,298,0,Normal,110,Y,Flat,1 269 | 34,M,ATA,98,220,0,Normal,150,N,Up,0 270 | 54,M,ASY,130,242,0,Normal,91,Y,Flat,1 271 | 47,F,NAP,130,235,0,Normal,145,N,Flat,0 272 | 45,M,ASY,120,225,0,Normal,140,N,Up,0 273 | 32,F,ATA,105,198,0,Normal,165,N,Up,0 274 | 55,M,ASY,140,201,0,Normal,130,Y,Flat,1 275 | 55,M,NAP,120,220,0,LVH,134,N,Up,0 276 | 45,F,ATA,180,295,0,Normal,180,N,Up,0 277 | 59,M,NAP,180,213,0,Normal,100,N,Up,0 278 | 51,M,NAP,135,160,0,Normal,150,N,Flat,1 279 | 52,M,ASY,170,223,0,Normal,126,Y,Flat,1 280 | 57,F,ASY,180,347,0,ST,126,Y,Flat,0 281 | 54,F,ATA,130,253,0,ST,155,N,Up,0 282 | 60,M,NAP,120,246,0,LVH,135,N,Up,0 283 | 49,M,ASY,150,222,0,Normal,122,N,Flat,1 284 | 51,F,NAP,130,220,0,Normal,160,Y,Up,0 285 | 55,F,ATA,110,344,0,ST,160,N,Up,0 286 | 42,M,ASY,140,358,0,Normal,170,N,Up,0 287 | 51,F,NAP,110,190,0,Normal,120,N,Up,0 288 | 59,M,ASY,140,169,0,Normal,140,N,Up,0 289 | 53,M,ATA,120,181,0,Normal,132,N,Up,0 290 | 48,F,ATA,133,308,0,ST,156,N,Up,0 291 | 36,M,ATA,120,166,0,Normal,180,N,Up,0 292 | 48,M,NAP,110,211,0,Normal,138,N,Up,0 293 | 47,F,ATA,140,257,0,Normal,135,N,Up,0 294 | 53,M,ASY,130,182,0,Normal,148,N,Up,0 295 | 65,M,ASY,115,0,0,Normal,93,Y,Flat,1 296 | 32,M,TA,95,0,1,Normal,127,N,Up,1 297 | 61,M,ASY,105,0,1,Normal,110,Y,Up,1 298 | 50,M,ASY,145,0,1,Normal,139,Y,Flat,1 299 | 57,M,ASY,110,0,1,ST,131,Y,Up,1 300 | 51,M,ASY,110,0,1,Normal,92,N,Flat,1 301 | 47,M,ASY,110,0,1,ST,149,N,Up,1 302 | 60,M,ASY,160,0,1,Normal,149,N,Flat,1 303 | 55,M,ATA,140,0,0,ST,150,N,Up,0 304 | 53,M,ASY,125,0,1,Normal,120,N,Up,1 305 | 62,F,ASY,120,0,1,ST,123,Y,Down,1 306 | 51,M,ASY,95,0,1,Normal,126,N,Flat,1 307 | 51,F,ASY,120,0,1,Normal,127,Y,Up,1 308 | 55,M,ASY,115,0,1,Normal,155,N,Flat,1 309 | 53,M,ATA,130,0,0,ST,120,N,Down,0 310 | 58,M,ASY,115,0,1,Normal,138,N,Up,1 311 | 57,M,ASY,95,0,1,Normal,182,N,Down,1 312 | 65,M,ASY,155,0,0,Normal,154,N,Up,0 313 | 60,M,ASY,125,0,1,Normal,110,N,Up,1 314 | 41,M,ASY,125,0,1,Normal,176,N,Up,1 315 | 34,M,ASY,115,0,1,Normal,154,N,Up,1 316 | 53,M,ASY,80,0,0,Normal,141,Y,Down,0 317 | 74,M,ATA,145,0,1,ST,123,N,Up,1 318 | 57,M,NAP,105,0,1,Normal,148,N,Flat,1 319 | 56,M,ASY,140,0,1,Normal,121,Y,Up,1 320 | 61,M,ASY,130,0,1,Normal,77,N,Flat,1 321 | 68,M,ASY,145,0,1,Normal,136,N,Up,1 322 | 59,M,NAP,125,0,1,Normal,175,N,Flat,1 323 | 63,M,ASY,100,0,1,Normal,109,N,Flat,1 324 | 38,F,ASY,105,0,1,Normal,166,N,Up,1 325 | 62,M,ASY,115,0,1,Normal,128,Y,Down,1 326 | 46,M,ASY,100,0,1,ST,133,N,Flat,1 327 | 42,M,ASY,105,0,1,Normal,128,Y,Down,1 328 | 45,M,NAP,110,0,0,Normal,138,N,Up,0 329 | 59,M,ASY,125,0,1,Normal,119,Y,Up,1 330 | 52,M,ASY,95,0,1,Normal,82,Y,Flat,1 331 | 60,M,ASY,130,0,1,ST,130,Y,Down,1 332 | 60,M,NAP,115,0,1,Normal,143,N,Up,1 333 | 56,M,ASY,115,0,1,ST,82,N,Up,1 334 | 38,M,NAP,100,0,0,Normal,179,N,Up,0 335 | 40,M,ASY,95,0,1,ST,144,N,Up,1 336 | 51,M,ASY,130,0,1,Normal,170,N,Up,1 337 | 62,M,TA,120,0,1,LVH,134,N,Flat,1 338 | 72,M,NAP,160,0,0,LVH,114,N,Flat,0 339 | 63,M,ASY,150,0,1,ST,154,N,Up,1 340 | 63,M,ASY,140,0,1,LVH,149,N,Up,1 341 | 64,F,ASY,95,0,1,Normal,145,N,Down,1 342 | 43,M,ASY,100,0,1,Normal,122,N,Down,1 343 | 64,M,ASY,110,0,1,Normal,114,Y,Down,1 344 | 61,M,ASY,110,0,1,Normal,113,N,Flat,1 345 | 52,M,ASY,130,0,1,Normal,120,N,Flat,1 346 | 51,M,ASY,120,0,1,Normal,104,N,Flat,1 347 | 69,M,ASY,135,0,0,Normal,130,N,Flat,1 348 | 59,M,ASY,120,0,0,Normal,115,N,Flat,1 349 | 48,M,ASY,115,0,1,Normal,128,N,Flat,1 350 | 69,M,ASY,137,0,0,ST,104,Y,Flat,1 351 | 36,M,ASY,110,0,1,Normal,125,Y,Flat,1 352 | 53,M,ASY,120,0,1,Normal,120,N,Flat,1 353 | 43,M,ASY,140,0,0,ST,140,Y,Up,1 354 | 56,M,ASY,120,0,0,ST,100,Y,Down,1 355 | 58,M,ASY,130,0,0,ST,100,Y,Flat,1 356 | 55,M,ASY,120,0,0,ST,92,N,Up,1 357 | 67,M,TA,145,0,0,LVH,125,N,Flat,1 358 | 46,M,ASY,115,0,0,Normal,113,Y,Flat,1 359 | 53,M,ATA,120,0,0,Normal,95,N,Flat,1 360 | 38,M,NAP,115,0,0,Normal,128,Y,Flat,1 361 | 53,M,NAP,105,0,0,Normal,115,N,Flat,1 362 | 62,M,NAP,160,0,0,Normal,72,Y,Flat,1 363 | 47,M,ASY,160,0,0,Normal,124,Y,Flat,1 364 | 56,M,NAP,155,0,0,ST,99,N,Flat,1 365 | 56,M,ASY,120,0,0,ST,148,N,Flat,1 366 | 56,M,NAP,120,0,0,Normal,97,N,Flat,0 367 | 64,F,ASY,200,0,0,Normal,140,Y,Flat,1 368 | 61,M,ASY,150,0,0,Normal,117,Y,Flat,1 369 | 68,M,ASY,135,0,0,ST,120,Y,Up,1 370 | 57,M,ASY,140,0,0,Normal,120,Y,Flat,1 371 | 63,M,ASY,150,0,0,Normal,86,Y,Flat,1 372 | 60,M,ASY,135,0,0,Normal,63,Y,Up,1 373 | 66,M,ASY,150,0,0,Normal,108,Y,Flat,1 374 | 63,M,ASY,185,0,0,Normal,98,Y,Up,1 375 | 59,M,ASY,135,0,0,Normal,115,Y,Flat,1 376 | 61,M,ASY,125,0,0,Normal,105,Y,Down,1 377 | 73,F,NAP,160,0,0,ST,121,N,Up,1 378 | 47,M,NAP,155,0,0,Normal,118,Y,Flat,1 379 | 65,M,ASY,160,0,1,ST,122,N,Flat,1 380 | 70,M,ASY,140,0,1,Normal,157,Y,Flat,1 381 | 50,M,ASY,120,0,0,ST,156,Y,Up,1 382 | 60,M,ASY,160,0,0,ST,99,Y,Flat,1 383 | 50,M,ASY,115,0,0,Normal,120,Y,Flat,1 384 | 43,M,ASY,115,0,0,Normal,145,Y,Flat,1 385 | 38,F,ASY,110,0,0,Normal,156,N,Flat,1 386 | 54,M,ASY,120,0,0,Normal,155,N,Flat,1 387 | 61,M,ASY,150,0,0,Normal,105,Y,Flat,1 388 | 42,M,ASY,145,0,0,Normal,99,Y,Flat,1 389 | 53,M,ASY,130,0,0,LVH,135,Y,Flat,1 390 | 55,M,ASY,140,0,0,Normal,83,N,Flat,1 391 | 61,M,ASY,160,0,1,ST,145,N,Flat,1 392 | 51,M,ASY,140,0,0,Normal,60,N,Flat,1 393 | 70,M,ASY,115,0,0,ST,92,Y,Flat,1 394 | 61,M,ASY,130,0,0,LVH,115,N,Flat,1 395 | 38,M,ASY,150,0,1,Normal,120,Y,Flat,1 396 | 57,M,ASY,160,0,1,Normal,98,Y,Flat,1 397 | 38,M,ASY,135,0,1,Normal,150,N,Flat,1 398 | 62,F,TA,140,0,1,Normal,143,N,Flat,1 399 | 58,M,ASY,170,0,1,ST,105,Y,Flat,1 400 | 52,M,ASY,165,0,1,Normal,122,Y,Up,1 401 | 61,M,NAP,200,0,1,ST,70,N,Flat,1 402 | 50,F,ASY,160,0,1,Normal,110,N,Flat,1 403 | 51,M,ASY,130,0,1,ST,163,N,Flat,1 404 | 65,M,ASY,145,0,1,ST,67,N,Flat,1 405 | 52,M,ASY,135,0,1,Normal,128,Y,Flat,1 406 | 47,M,NAP,110,0,1,Normal,120,Y,Flat,1 407 | 35,M,ASY,120,0,1,Normal,130,Y,Flat,1 408 | 57,M,ASY,140,0,1,Normal,100,Y,Flat,1 409 | 62,M,ASY,115,0,1,Normal,72,Y,Flat,1 410 | 59,M,ASY,110,0,1,Normal,94,N,Flat,1 411 | 53,M,NAP,160,0,1,LVH,122,Y,Flat,1 412 | 62,M,ASY,150,0,1,ST,78,N,Flat,1 413 | 54,M,ASY,180,0,1,Normal,150,N,Flat,1 414 | 56,M,ASY,125,0,1,Normal,103,Y,Flat,1 415 | 56,M,NAP,125,0,1,Normal,98,N,Flat,1 416 | 54,M,ASY,130,0,1,Normal,110,Y,Flat,1 417 | 66,F,ASY,155,0,1,Normal,90,N,Flat,1 418 | 63,M,ASY,140,260,0,ST,112,Y,Flat,1 419 | 44,M,ASY,130,209,0,ST,127,N,Up,0 420 | 60,M,ASY,132,218,0,ST,140,Y,Down,1 421 | 55,M,ASY,142,228,0,ST,149,Y,Up,1 422 | 66,M,NAP,110,213,1,LVH,99,Y,Flat,0 423 | 66,M,NAP,120,0,0,ST,120,N,Up,0 424 | 65,M,ASY,150,236,1,ST,105,Y,Flat,1 425 | 60,M,NAP,180,0,0,ST,140,Y,Flat,0 426 | 60,M,NAP,120,0,1,Normal,141,Y,Up,1 427 | 60,M,ATA,160,267,1,ST,157,N,Flat,1 428 | 56,M,ATA,126,166,0,ST,140,N,Up,0 429 | 59,M,ASY,140,0,0,ST,117,Y,Flat,1 430 | 62,M,ASY,110,0,0,Normal,120,Y,Flat,1 431 | 63,M,NAP,133,0,0,LVH,120,Y,Flat,1 432 | 57,M,ASY,128,0,1,ST,148,Y,Flat,1 433 | 62,M,ASY,120,220,0,ST,86,N,Up,0 434 | 63,M,ASY,170,177,0,Normal,84,Y,Down,1 435 | 46,M,ASY,110,236,0,Normal,125,Y,Flat,1 436 | 63,M,ASY,126,0,0,ST,120,N,Down,0 437 | 60,M,ASY,152,0,0,ST,118,Y,Up,0 438 | 58,M,ASY,116,0,0,Normal,124,N,Up,1 439 | 64,M,ASY,120,0,1,ST,106,N,Flat,1 440 | 63,M,NAP,130,0,0,ST,111,Y,Flat,1 441 | 74,M,NAP,138,0,0,Normal,116,N,Up,0 442 | 52,M,NAP,128,0,0,ST,180,N,Up,1 443 | 69,M,ASY,130,0,1,ST,129,N,Flat,1 444 | 51,M,ASY,128,0,1,ST,125,Y,Flat,1 445 | 60,M,ASY,130,186,1,ST,140,Y,Flat,1 446 | 56,M,ASY,120,100,0,Normal,120,Y,Flat,1 447 | 55,M,NAP,136,228,0,ST,124,Y,Flat,1 448 | 54,M,ASY,130,0,0,ST,117,Y,Flat,1 449 | 77,M,ASY,124,171,0,ST,110,Y,Up,1 450 | 63,M,ASY,160,230,1,Normal,105,Y,Flat,1 451 | 55,M,NAP,0,0,0,Normal,155,N,Flat,1 452 | 52,M,NAP,122,0,0,Normal,110,Y,Down,1 453 | 64,M,ASY,144,0,0,ST,122,Y,Flat,1 454 | 60,M,ASY,140,281,0,ST,118,Y,Flat,1 455 | 60,M,ASY,120,0,0,Normal,133,Y,Up,0 456 | 58,M,ASY,136,203,1,Normal,123,Y,Flat,1 457 | 59,M,ASY,154,0,0,ST,131,Y,Up,0 458 | 61,M,NAP,120,0,0,Normal,80,Y,Flat,1 459 | 40,M,ASY,125,0,1,Normal,165,N,Flat,1 460 | 61,M,ASY,134,0,1,ST,86,N,Flat,1 461 | 41,M,ASY,104,0,0,ST,111,N,Up,0 462 | 57,M,ASY,139,277,1,ST,118,Y,Flat,1 463 | 63,M,ASY,136,0,0,Normal,84,Y,Flat,1 464 | 59,M,ASY,122,233,0,Normal,117,Y,Down,1 465 | 51,M,ASY,128,0,0,Normal,107,N,Up,0 466 | 59,M,NAP,131,0,0,Normal,128,Y,Down,1 467 | 42,M,NAP,134,240,0,Normal,160,N,Up,0 468 | 55,M,NAP,120,0,0,ST,125,Y,Flat,1 469 | 63,F,ATA,132,0,0,Normal,130,N,Up,0 470 | 62,M,ASY,152,153,0,ST,97,Y,Up,1 471 | 56,M,ATA,124,224,1,Normal,161,N,Flat,0 472 | 53,M,ASY,126,0,0,Normal,106,N,Flat,1 473 | 68,M,ASY,138,0,0,Normal,130,Y,Flat,1 474 | 53,M,ASY,154,0,1,ST,140,Y,Flat,1 475 | 60,M,NAP,141,316,1,ST,122,Y,Flat,1 476 | 62,M,ATA,131,0,0,Normal,130,N,Up,0 477 | 59,M,ASY,178,0,1,LVH,120,Y,Flat,1 478 | 51,M,ASY,132,218,1,LVH,139,N,Up,0 479 | 61,M,ASY,110,0,1,Normal,108,Y,Down,1 480 | 57,M,ASY,130,311,1,ST,148,Y,Flat,1 481 | 56,M,NAP,170,0,0,LVH,123,Y,Flat,1 482 | 58,M,ATA,126,0,1,Normal,110,Y,Flat,1 483 | 69,M,NAP,140,0,1,ST,118,N,Down,1 484 | 67,M,TA,142,270,1,Normal,125,N,Up,1 485 | 58,M,ASY,120,0,0,LVH,106,Y,Down,1 486 | 65,M,ASY,134,0,0,Normal,112,Y,Flat,1 487 | 63,M,ATA,139,217,1,ST,128,Y,Flat,1 488 | 55,M,ATA,110,214,1,ST,180,N,Up,0 489 | 57,M,ASY,140,214,0,ST,144,Y,Flat,1 490 | 65,M,TA,140,252,0,Normal,135,N,Up,0 491 | 54,M,ASY,136,220,0,Normal,140,Y,Flat,1 492 | 72,M,NAP,120,214,0,Normal,102,Y,Flat,1 493 | 75,M,ASY,170,203,1,ST,108,N,Flat,1 494 | 49,M,TA,130,0,0,ST,145,N,Flat,1 495 | 51,M,NAP,137,339,0,Normal,127,Y,Flat,1 496 | 60,M,ASY,142,216,0,Normal,110,Y,Flat,1 497 | 64,F,ASY,142,276,0,Normal,140,Y,Flat,1 498 | 58,M,ASY,132,458,1,Normal,69,N,Down,0 499 | 61,M,ASY,146,241,0,Normal,148,Y,Down,1 500 | 67,M,ASY,160,384,1,ST,130,Y,Flat,1 501 | 62,M,ASY,135,297,0,Normal,130,Y,Flat,1 502 | 65,M,ASY,136,248,0,Normal,140,Y,Down,1 503 | 63,M,ASY,130,308,0,Normal,138,Y,Flat,1 504 | 69,M,ASY,140,208,0,ST,140,Y,Flat,1 505 | 51,M,ASY,132,227,1,ST,138,N,Up,0 506 | 62,M,ASY,158,210,1,Normal,112,Y,Down,1 507 | 55,M,NAP,136,245,1,ST,131,Y,Flat,1 508 | 75,M,ASY,136,225,0,Normal,112,Y,Flat,1 509 | 40,M,NAP,106,240,0,Normal,80,Y,Up,0 510 | 67,M,ASY,120,0,1,Normal,150,N,Down,1 511 | 58,M,ASY,110,198,0,Normal,110,N,Flat,1 512 | 60,M,ASY,136,195,0,Normal,126,N,Up,0 513 | 63,M,ASY,160,267,1,ST,88,Y,Flat,1 514 | 35,M,NAP,123,161,0,ST,153,N,Up,0 515 | 62,M,TA,112,258,0,ST,150,Y,Flat,1 516 | 43,M,ASY,122,0,0,Normal,120,N,Up,1 517 | 63,M,NAP,130,0,1,ST,160,N,Flat,0 518 | 68,M,NAP,150,195,1,Normal,132,N,Flat,1 519 | 65,M,ASY,150,235,0,Normal,120,Y,Flat,1 520 | 48,M,NAP,102,0,1,ST,110,Y,Down,1 521 | 63,M,ASY,96,305,0,ST,121,Y,Up,1 522 | 64,M,ASY,130,223,0,ST,128,N,Flat,0 523 | 61,M,ASY,120,282,0,ST,135,Y,Down,1 524 | 50,M,ASY,144,349,0,LVH,120,Y,Up,1 525 | 59,M,ASY,124,160,0,Normal,117,Y,Flat,1 526 | 55,M,ASY,150,160,0,ST,150,N,Up,0 527 | 45,M,NAP,130,236,0,Normal,144,N,Up,0 528 | 65,M,ASY,144,312,0,LVH,113,Y,Flat,1 529 | 61,M,ATA,139,283,0,Normal,135,N,Up,0 530 | 49,M,NAP,131,142,0,Normal,127,Y,Flat,1 531 | 72,M,ASY,143,211,0,Normal,109,Y,Flat,1 532 | 50,M,ASY,133,218,0,Normal,128,Y,Flat,1 533 | 64,M,ASY,143,306,1,ST,115,Y,Flat,1 534 | 55,M,ASY,116,186,1,ST,102,N,Flat,1 535 | 63,M,ASY,110,252,0,ST,140,Y,Flat,1 536 | 59,M,ASY,125,222,0,Normal,135,Y,Down,1 537 | 56,M,ASY,130,0,0,LVH,122,Y,Flat,1 538 | 62,M,NAP,133,0,1,ST,119,Y,Flat,1 539 | 74,M,ASY,150,258,1,ST,130,Y,Down,1 540 | 54,M,ASY,130,202,1,Normal,112,Y,Flat,1 541 | 57,M,ASY,110,197,0,LVH,100,N,Up,0 542 | 62,M,NAP,138,204,0,ST,122,Y,Flat,1 543 | 76,M,NAP,104,113,0,LVH,120,N,Down,1 544 | 54,F,ASY,138,274,0,Normal,105,Y,Flat,1 545 | 70,M,ASY,170,192,0,ST,129,Y,Down,1 546 | 61,F,ATA,140,298,1,Normal,120,Y,Up,0 547 | 48,M,ASY,132,272,0,ST,139,N,Up,0 548 | 48,M,NAP,132,220,1,ST,162,N,Flat,1 549 | 61,M,TA,142,200,1,ST,100,N,Down,1 550 | 66,M,ASY,112,261,0,Normal,140,N,Up,1 551 | 68,M,TA,139,181,1,ST,135,N,Up,0 552 | 55,M,ASY,172,260,0,Normal,73,N,Flat,1 553 | 62,M,NAP,120,220,0,LVH,86,N,Up,0 554 | 71,M,NAP,144,221,0,Normal,108,Y,Flat,1 555 | 74,M,TA,145,216,1,Normal,116,Y,Flat,1 556 | 53,M,NAP,155,175,1,ST,160,N,Up,0 557 | 58,M,NAP,150,219,0,ST,118,Y,Flat,1 558 | 75,M,ASY,160,310,1,Normal,112,Y,Down,0 559 | 56,M,NAP,137,208,1,ST,122,Y,Flat,1 560 | 58,M,NAP,137,232,0,ST,124,Y,Flat,1 561 | 64,M,ASY,134,273,0,Normal,102,Y,Down,1 562 | 54,M,NAP,133,203,0,ST,137,N,Up,0 563 | 54,M,ATA,132,182,0,ST,141,N,Up,0 564 | 59,M,ASY,140,274,0,Normal,154,Y,Flat,0 565 | 55,M,ASY,135,204,1,ST,126,Y,Flat,1 566 | 57,M,ASY,144,270,1,ST,160,Y,Flat,1 567 | 61,M,ASY,141,292,0,ST,115,Y,Flat,1 568 | 41,M,ASY,150,171,0,Normal,128,Y,Flat,0 569 | 71,M,ASY,130,221,0,ST,115,Y,Flat,1 570 | 38,M,ASY,110,289,0,Normal,105,Y,Down,1 571 | 55,M,ASY,158,217,0,Normal,110,Y,Flat,1 572 | 56,M,ASY,128,223,0,ST,119,Y,Down,1 573 | 69,M,ASY,140,110,1,Normal,109,Y,Flat,1 574 | 64,M,ASY,150,193,0,ST,135,Y,Flat,1 575 | 72,M,ASY,160,123,1,LVH,130,N,Flat,1 576 | 69,M,ASY,142,210,1,ST,112,Y,Flat,1 577 | 56,M,ASY,137,282,1,Normal,126,Y,Flat,1 578 | 62,M,ASY,139,170,0,ST,120,Y,Flat,1 579 | 67,M,ASY,146,369,0,Normal,110,Y,Flat,1 580 | 57,M,ASY,156,173,0,LVH,119,Y,Down,1 581 | 69,M,ASY,145,289,1,ST,110,Y,Flat,1 582 | 51,M,ASY,131,152,1,LVH,130,Y,Flat,1 583 | 48,M,ASY,140,208,0,Normal,159,Y,Up,1 584 | 69,M,ASY,122,216,1,LVH,84,Y,Flat,1 585 | 69,M,NAP,142,271,0,LVH,126,N,Up,0 586 | 64,M,ASY,141,244,1,ST,116,Y,Flat,1 587 | 57,M,ATA,180,285,1,ST,120,N,Flat,1 588 | 53,M,ASY,124,243,0,Normal,122,Y,Flat,1 589 | 37,M,NAP,118,240,0,LVH,165,N,Flat,0 590 | 67,M,ASY,140,219,0,ST,122,Y,Flat,1 591 | 74,M,NAP,140,237,1,Normal,94,N,Flat,1 592 | 63,M,ATA,136,165,0,ST,133,N,Up,0 593 | 58,M,ASY,100,213,0,ST,110,N,Up,0 594 | 61,M,ASY,190,287,1,LVH,150,Y,Down,1 595 | 64,M,ASY,130,258,1,LVH,130,N,Flat,1 596 | 58,M,ASY,160,256,1,LVH,113,Y,Up,1 597 | 60,M,ASY,130,186,1,LVH,140,Y,Flat,1 598 | 57,M,ASY,122,264,0,LVH,100,N,Flat,1 599 | 55,M,NAP,133,185,0,ST,136,N,Up,0 600 | 55,M,ASY,120,226,0,LVH,127,Y,Down,1 601 | 56,M,ASY,130,203,1,Normal,98,N,Flat,1 602 | 57,M,ASY,130,207,0,ST,96,Y,Flat,0 603 | 61,M,NAP,140,284,0,Normal,123,Y,Flat,1 604 | 61,M,NAP,120,337,0,Normal,98,Y,Flat,1 605 | 74,M,ASY,155,310,0,Normal,112,Y,Down,1 606 | 68,M,NAP,134,254,1,Normal,151,Y,Up,0 607 | 51,F,ASY,114,258,1,LVH,96,N,Up,0 608 | 62,M,ASY,160,254,1,ST,108,Y,Flat,1 609 | 53,M,ASY,144,300,1,ST,128,Y,Flat,1 610 | 62,M,ASY,158,170,0,ST,138,Y,Flat,1 611 | 46,M,ASY,134,310,0,Normal,126,N,Flat,1 612 | 54,F,ASY,127,333,1,ST,154,N,Flat,1 613 | 62,M,TA,135,139,0,ST,137,N,Up,0 614 | 55,M,ASY,122,223,1,ST,100,N,Flat,1 615 | 58,M,ASY,140,385,1,LVH,135,N,Up,0 616 | 62,M,ATA,120,254,0,LVH,93,Y,Flat,1 617 | 70,M,ASY,130,322,0,LVH,109,N,Flat,1 618 | 67,F,NAP,115,564,0,LVH,160,N,Flat,0 619 | 57,M,ATA,124,261,0,Normal,141,N,Up,1 620 | 64,M,ASY,128,263,0,Normal,105,Y,Flat,0 621 | 74,F,ATA,120,269,0,LVH,121,Y,Up,0 622 | 65,M,ASY,120,177,0,Normal,140,N,Up,0 623 | 56,M,NAP,130,256,1,LVH,142,Y,Flat,1 624 | 59,M,ASY,110,239,0,LVH,142,Y,Flat,1 625 | 60,M,ASY,140,293,0,LVH,170,N,Flat,1 626 | 63,F,ASY,150,407,0,LVH,154,N,Flat,1 627 | 59,M,ASY,135,234,0,Normal,161,N,Flat,0 628 | 53,M,ASY,142,226,0,LVH,111,Y,Up,0 629 | 44,M,NAP,140,235,0,LVH,180,N,Up,0 630 | 61,M,TA,134,234,0,Normal,145,N,Flat,1 631 | 57,F,ASY,128,303,0,LVH,159,N,Up,0 632 | 71,F,ASY,112,149,0,Normal,125,N,Flat,0 633 | 46,M,ASY,140,311,0,Normal,120,Y,Flat,1 634 | 53,M,ASY,140,203,1,LVH,155,Y,Down,1 635 | 64,M,TA,110,211,0,LVH,144,Y,Flat,0 636 | 40,M,TA,140,199,0,Normal,178,Y,Up,0 637 | 67,M,ASY,120,229,0,LVH,129,Y,Flat,1 638 | 48,M,ATA,130,245,0,LVH,180,N,Flat,0 639 | 43,M,ASY,115,303,0,Normal,181,N,Flat,0 640 | 47,M,ASY,112,204,0,Normal,143,N,Up,0 641 | 54,F,ATA,132,288,1,LVH,159,Y,Up,0 642 | 48,F,NAP,130,275,0,Normal,139,N,Up,0 643 | 46,F,ASY,138,243,0,LVH,152,Y,Flat,0 644 | 51,F,NAP,120,295,0,LVH,157,N,Up,0 645 | 58,M,NAP,112,230,0,LVH,165,N,Flat,1 646 | 71,F,NAP,110,265,1,LVH,130,N,Up,0 647 | 57,M,NAP,128,229,0,LVH,150,N,Flat,1 648 | 66,M,ASY,160,228,0,LVH,138,N,Up,0 649 | 37,F,NAP,120,215,0,Normal,170,N,Up,0 650 | 59,M,ASY,170,326,0,LVH,140,Y,Down,1 651 | 50,M,ASY,144,200,0,LVH,126,Y,Flat,1 652 | 48,M,ASY,130,256,1,LVH,150,Y,Up,1 653 | 61,M,ASY,140,207,0,LVH,138,Y,Up,1 654 | 59,M,TA,160,273,0,LVH,125,N,Up,1 655 | 42,M,NAP,130,180,0,Normal,150,N,Up,0 656 | 48,M,ASY,122,222,0,LVH,186,N,Up,0 657 | 40,M,ASY,152,223,0,Normal,181,N,Up,1 658 | 62,F,ASY,124,209,0,Normal,163,N,Up,0 659 | 44,M,NAP,130,233,0,Normal,179,Y,Up,0 660 | 46,M,ATA,101,197,1,Normal,156,N,Up,0 661 | 59,M,NAP,126,218,1,Normal,134,N,Flat,1 662 | 58,M,NAP,140,211,1,LVH,165,N,Up,0 663 | 49,M,NAP,118,149,0,LVH,126,N,Up,1 664 | 44,M,ASY,110,197,0,LVH,177,N,Up,1 665 | 66,M,ATA,160,246,0,Normal,120,Y,Flat,1 666 | 65,F,ASY,150,225,0,LVH,114,N,Flat,1 667 | 42,M,ASY,136,315,0,Normal,125,Y,Flat,1 668 | 52,M,ATA,128,205,1,Normal,184,N,Up,0 669 | 65,F,NAP,140,417,1,LVH,157,N,Up,0 670 | 63,F,ATA,140,195,0,Normal,179,N,Up,0 671 | 45,F,ATA,130,234,0,LVH,175,N,Flat,0 672 | 41,F,ATA,105,198,0,Normal,168,N,Up,0 673 | 61,M,ASY,138,166,0,LVH,125,Y,Flat,1 674 | 60,F,NAP,120,178,1,Normal,96,N,Up,0 675 | 59,F,ASY,174,249,0,Normal,143,Y,Flat,1 676 | 62,M,ATA,120,281,0,LVH,103,N,Flat,1 677 | 57,M,NAP,150,126,1,Normal,173,N,Up,0 678 | 51,F,ASY,130,305,0,Normal,142,Y,Flat,1 679 | 44,M,NAP,120,226,0,Normal,169,N,Up,0 680 | 60,F,TA,150,240,0,Normal,171,N,Up,0 681 | 63,M,TA,145,233,1,LVH,150,N,Down,0 682 | 57,M,ASY,150,276,0,LVH,112,Y,Flat,1 683 | 51,M,ASY,140,261,0,LVH,186,Y,Up,0 684 | 58,F,ATA,136,319,1,LVH,152,N,Up,1 685 | 44,F,NAP,118,242,0,Normal,149,N,Flat,0 686 | 47,M,NAP,108,243,0,Normal,152,N,Up,1 687 | 61,M,ASY,120,260,0,Normal,140,Y,Flat,1 688 | 57,F,ASY,120,354,0,Normal,163,Y,Up,0 689 | 70,M,ATA,156,245,0,LVH,143,N,Up,0 690 | 76,F,NAP,140,197,0,ST,116,N,Flat,0 691 | 67,F,ASY,106,223,0,Normal,142,N,Up,0 692 | 45,M,ASY,142,309,0,LVH,147,Y,Flat,1 693 | 45,M,ASY,104,208,0,LVH,148,Y,Flat,0 694 | 39,F,NAP,94,199,0,Normal,179,N,Up,0 695 | 42,F,NAP,120,209,0,Normal,173,N,Flat,0 696 | 56,M,ATA,120,236,0,Normal,178,N,Up,0 697 | 58,M,ASY,146,218,0,Normal,105,N,Flat,1 698 | 35,M,ASY,120,198,0,Normal,130,Y,Flat,1 699 | 58,M,ASY,150,270,0,LVH,111,Y,Up,1 700 | 41,M,NAP,130,214,0,LVH,168,N,Flat,0 701 | 57,M,ASY,110,201,0,Normal,126,Y,Flat,0 702 | 42,M,TA,148,244,0,LVH,178,N,Up,0 703 | 62,M,ATA,128,208,1,LVH,140,N,Up,0 704 | 59,M,TA,178,270,0,LVH,145,N,Down,0 705 | 41,F,ATA,126,306,0,Normal,163,N,Up,0 706 | 50,M,ASY,150,243,0,LVH,128,N,Flat,1 707 | 59,M,ATA,140,221,0,Normal,164,Y,Up,0 708 | 61,F,ASY,130,330,0,LVH,169,N,Up,1 709 | 54,M,ASY,124,266,0,LVH,109,Y,Flat,1 710 | 54,M,ASY,110,206,0,LVH,108,Y,Flat,1 711 | 52,M,ASY,125,212,0,Normal,168,N,Up,1 712 | 47,M,ASY,110,275,0,LVH,118,Y,Flat,1 713 | 66,M,ASY,120,302,0,LVH,151,N,Flat,0 714 | 58,M,ASY,100,234,0,Normal,156,N,Up,1 715 | 64,F,NAP,140,313,0,Normal,133,N,Up,0 716 | 50,F,ATA,120,244,0,Normal,162,N,Up,0 717 | 44,F,NAP,108,141,0,Normal,175,N,Flat,0 718 | 67,M,ASY,120,237,0,Normal,71,N,Flat,1 719 | 49,F,ASY,130,269,0,Normal,163,N,Up,0 720 | 57,M,ASY,165,289,1,LVH,124,N,Flat,1 721 | 63,M,ASY,130,254,0,LVH,147,N,Flat,1 722 | 48,M,ASY,124,274,0,LVH,166,N,Flat,1 723 | 51,M,NAP,100,222,0,Normal,143,Y,Flat,0 724 | 60,F,ASY,150,258,0,LVH,157,N,Flat,1 725 | 59,M,ASY,140,177,0,Normal,162,Y,Up,1 726 | 45,F,ATA,112,160,0,Normal,138,N,Flat,0 727 | 55,F,ASY,180,327,0,ST,117,Y,Flat,1 728 | 41,M,ATA,110,235,0,Normal,153,N,Up,0 729 | 60,F,ASY,158,305,0,LVH,161,N,Up,1 730 | 54,F,NAP,135,304,1,Normal,170,N,Up,0 731 | 42,M,ATA,120,295,0,Normal,162,N,Up,0 732 | 49,F,ATA,134,271,0,Normal,162,N,Flat,0 733 | 46,M,ASY,120,249,0,LVH,144,N,Up,1 734 | 56,F,ASY,200,288,1,LVH,133,Y,Down,1 735 | 66,F,TA,150,226,0,Normal,114,N,Down,0 736 | 56,M,ASY,130,283,1,LVH,103,Y,Down,1 737 | 49,M,NAP,120,188,0,Normal,139,N,Flat,1 738 | 54,M,ASY,122,286,0,LVH,116,Y,Flat,1 739 | 57,M,ASY,152,274,0,Normal,88,Y,Flat,1 740 | 65,F,NAP,160,360,0,LVH,151,N,Up,0 741 | 54,M,NAP,125,273,0,LVH,152,N,Down,0 742 | 54,F,NAP,160,201,0,Normal,163,N,Up,0 743 | 62,M,ASY,120,267,0,Normal,99,Y,Flat,1 744 | 52,F,NAP,136,196,0,LVH,169,N,Flat,0 745 | 52,M,ATA,134,201,0,Normal,158,N,Up,0 746 | 60,M,ASY,117,230,1,Normal,160,Y,Up,1 747 | 63,F,ASY,108,269,0,Normal,169,Y,Flat,1 748 | 66,M,ASY,112,212,0,LVH,132,Y,Up,1 749 | 42,M,ASY,140,226,0,Normal,178,N,Up,0 750 | 64,M,ASY,120,246,0,LVH,96,Y,Down,1 751 | 54,M,NAP,150,232,0,LVH,165,N,Up,0 752 | 46,F,NAP,142,177,0,LVH,160,Y,Down,0 753 | 67,F,NAP,152,277,0,Normal,172,N,Up,0 754 | 56,M,ASY,125,249,1,LVH,144,Y,Flat,1 755 | 34,F,ATA,118,210,0,Normal,192,N,Up,0 756 | 57,M,ASY,132,207,0,Normal,168,Y,Up,0 757 | 64,M,ASY,145,212,0,LVH,132,N,Flat,1 758 | 59,M,ASY,138,271,0,LVH,182,N,Up,0 759 | 50,M,NAP,140,233,0,Normal,163,N,Flat,1 760 | 51,M,TA,125,213,0,LVH,125,Y,Up,0 761 | 54,M,ATA,192,283,0,LVH,195,N,Up,1 762 | 53,M,ASY,123,282,0,Normal,95,Y,Flat,1 763 | 52,M,ASY,112,230,0,Normal,160,N,Up,1 764 | 40,M,ASY,110,167,0,LVH,114,Y,Flat,1 765 | 58,M,NAP,132,224,0,LVH,173,N,Up,1 766 | 41,F,NAP,112,268,0,LVH,172,Y,Up,0 767 | 41,M,NAP,112,250,0,Normal,179,N,Up,0 768 | 50,F,NAP,120,219,0,Normal,158,N,Flat,0 769 | 54,F,NAP,108,267,0,LVH,167,N,Up,0 770 | 64,F,ASY,130,303,0,Normal,122,N,Flat,0 771 | 51,F,NAP,130,256,0,LVH,149,N,Up,0 772 | 46,F,ATA,105,204,0,Normal,172,N,Up,0 773 | 55,M,ASY,140,217,0,Normal,111,Y,Down,1 774 | 45,M,ATA,128,308,0,LVH,170,N,Up,0 775 | 56,M,TA,120,193,0,LVH,162,N,Flat,0 776 | 66,F,ASY,178,228,1,Normal,165,Y,Flat,1 777 | 38,M,TA,120,231,0,Normal,182,Y,Flat,1 778 | 62,F,ASY,150,244,0,Normal,154,Y,Flat,1 779 | 55,M,ATA,130,262,0,Normal,155,N,Up,0 780 | 58,M,ASY,128,259,0,LVH,130,Y,Flat,1 781 | 43,M,ASY,110,211,0,Normal,161,N,Up,0 782 | 64,F,ASY,180,325,0,Normal,154,Y,Up,0 783 | 50,F,ASY,110,254,0,LVH,159,N,Up,0 784 | 53,M,NAP,130,197,1,LVH,152,N,Down,0 785 | 45,F,ASY,138,236,0,LVH,152,Y,Flat,0 786 | 65,M,TA,138,282,1,LVH,174,N,Flat,1 787 | 69,M,TA,160,234,1,LVH,131,N,Flat,0 788 | 69,M,NAP,140,254,0,LVH,146,N,Flat,1 789 | 67,M,ASY,100,299,0,LVH,125,Y,Flat,1 790 | 68,F,NAP,120,211,0,LVH,115,N,Flat,0 791 | 34,M,TA,118,182,0,LVH,174,N,Up,0 792 | 62,F,ASY,138,294,1,Normal,106,N,Flat,1 793 | 51,M,ASY,140,298,0,Normal,122,Y,Flat,1 794 | 46,M,NAP,150,231,0,Normal,147,N,Flat,1 795 | 67,M,ASY,125,254,1,Normal,163,N,Flat,1 796 | 50,M,NAP,129,196,0,Normal,163,N,Up,0 797 | 42,M,NAP,120,240,1,Normal,194,N,Down,0 798 | 56,F,ASY,134,409,0,LVH,150,Y,Flat,1 799 | 41,M,ASY,110,172,0,LVH,158,N,Up,1 800 | 42,F,ASY,102,265,0,LVH,122,N,Flat,0 801 | 53,M,NAP,130,246,1,LVH,173,N,Up,0 802 | 43,M,NAP,130,315,0,Normal,162,N,Up,0 803 | 56,M,ASY,132,184,0,LVH,105,Y,Flat,1 804 | 52,M,ASY,108,233,1,Normal,147,N,Up,0 805 | 62,F,ASY,140,394,0,LVH,157,N,Flat,0 806 | 70,M,NAP,160,269,0,Normal,112,Y,Flat,1 807 | 54,M,ASY,140,239,0,Normal,160,N,Up,0 808 | 70,M,ASY,145,174,0,Normal,125,Y,Down,1 809 | 54,M,ATA,108,309,0,Normal,156,N,Up,0 810 | 35,M,ASY,126,282,0,LVH,156,Y,Up,1 811 | 48,M,NAP,124,255,1,Normal,175,N,Up,0 812 | 55,F,ATA,135,250,0,LVH,161,N,Flat,0 813 | 58,F,ASY,100,248,0,LVH,122,N,Flat,0 814 | 54,F,NAP,110,214,0,Normal,158,N,Flat,0 815 | 69,F,TA,140,239,0,Normal,151,N,Up,0 816 | 77,M,ASY,125,304,0,LVH,162,Y,Up,1 817 | 68,M,NAP,118,277,0,Normal,151,N,Up,0 818 | 58,M,ASY,125,300,0,LVH,171,N,Up,1 819 | 60,M,ASY,125,258,0,LVH,141,Y,Flat,1 820 | 51,M,ASY,140,299,0,Normal,173,Y,Up,1 821 | 55,M,ASY,160,289,0,LVH,145,Y,Flat,1 822 | 52,M,TA,152,298,1,Normal,178,N,Flat,0 823 | 60,F,NAP,102,318,0,Normal,160,N,Up,0 824 | 58,M,NAP,105,240,0,LVH,154,Y,Flat,0 825 | 64,M,NAP,125,309,0,Normal,131,Y,Flat,1 826 | 37,M,NAP,130,250,0,Normal,187,N,Down,0 827 | 59,M,TA,170,288,0,LVH,159,N,Flat,1 828 | 51,M,NAP,125,245,1,LVH,166,N,Flat,0 829 | 43,F,NAP,122,213,0,Normal,165,N,Flat,0 830 | 58,M,ASY,128,216,0,LVH,131,Y,Flat,1 831 | 29,M,ATA,130,204,0,LVH,202,N,Up,0 832 | 41,F,ATA,130,204,0,LVH,172,N,Up,0 833 | 63,F,NAP,135,252,0,LVH,172,N,Up,0 834 | 51,M,NAP,94,227,0,Normal,154,Y,Up,0 835 | 54,M,NAP,120,258,0,LVH,147,N,Flat,0 836 | 44,M,ATA,120,220,0,Normal,170,N,Up,0 837 | 54,M,ASY,110,239,0,Normal,126,Y,Flat,1 838 | 65,M,ASY,135,254,0,LVH,127,N,Flat,1 839 | 57,M,NAP,150,168,0,Normal,174,N,Up,0 840 | 63,M,ASY,130,330,1,LVH,132,Y,Up,1 841 | 35,F,ASY,138,183,0,Normal,182,N,Up,0 842 | 41,M,ATA,135,203,0,Normal,132,N,Flat,0 843 | 62,F,NAP,130,263,0,Normal,97,N,Flat,1 844 | 43,F,ASY,132,341,1,LVH,136,Y,Flat,1 845 | 58,F,TA,150,283,1,LVH,162,N,Up,0 846 | 52,M,TA,118,186,0,LVH,190,N,Flat,0 847 | 61,F,ASY,145,307,0,LVH,146,Y,Flat,1 848 | 39,M,ASY,118,219,0,Normal,140,N,Flat,1 849 | 45,M,ASY,115,260,0,LVH,185,N,Up,0 850 | 52,M,ASY,128,255,0,Normal,161,Y,Up,1 851 | 62,M,NAP,130,231,0,Normal,146,N,Flat,0 852 | 62,F,ASY,160,164,0,LVH,145,N,Down,1 853 | 53,F,ASY,138,234,0,LVH,160,N,Up,0 854 | 43,M,ASY,120,177,0,LVH,120,Y,Flat,1 855 | 47,M,NAP,138,257,0,LVH,156,N,Up,0 856 | 52,M,ATA,120,325,0,Normal,172,N,Up,0 857 | 68,M,NAP,180,274,1,LVH,150,Y,Flat,1 858 | 39,M,NAP,140,321,0,LVH,182,N,Up,0 859 | 53,F,ASY,130,264,0,LVH,143,N,Flat,0 860 | 62,F,ASY,140,268,0,LVH,160,N,Down,1 861 | 51,F,NAP,140,308,0,LVH,142,N,Up,0 862 | 60,M,ASY,130,253,0,Normal,144,Y,Up,1 863 | 65,M,ASY,110,248,0,LVH,158,N,Up,1 864 | 65,F,NAP,155,269,0,Normal,148,N,Up,0 865 | 60,M,NAP,140,185,0,LVH,155,N,Flat,1 866 | 60,M,ASY,145,282,0,LVH,142,Y,Flat,1 867 | 54,M,ASY,120,188,0,Normal,113,N,Flat,1 868 | 44,M,ATA,130,219,0,LVH,188,N,Up,0 869 | 44,M,ASY,112,290,0,LVH,153,N,Up,1 870 | 51,M,NAP,110,175,0,Normal,123,N,Up,0 871 | 59,M,NAP,150,212,1,Normal,157,N,Up,0 872 | 71,F,ATA,160,302,0,Normal,162,N,Up,0 873 | 61,M,NAP,150,243,1,Normal,137,Y,Flat,0 874 | 55,M,ASY,132,353,0,Normal,132,Y,Flat,1 875 | 64,M,NAP,140,335,0,Normal,158,N,Up,1 876 | 43,M,ASY,150,247,0,Normal,171,N,Up,0 877 | 58,F,NAP,120,340,0,Normal,172,N,Up,0 878 | 60,M,ASY,130,206,0,LVH,132,Y,Flat,1 879 | 58,M,ATA,120,284,0,LVH,160,N,Flat,1 880 | 49,M,ATA,130,266,0,Normal,171,N,Up,0 881 | 48,M,ATA,110,229,0,Normal,168,N,Down,1 882 | 52,M,NAP,172,199,1,Normal,162,N,Up,0 883 | 44,M,ATA,120,263,0,Normal,173,N,Up,0 884 | 56,F,ATA,140,294,0,LVH,153,N,Flat,0 885 | 57,M,ASY,140,192,0,Normal,148,N,Flat,0 886 | 67,M,ASY,160,286,0,LVH,108,Y,Flat,1 887 | 53,F,NAP,128,216,0,LVH,115,N,Up,0 888 | 52,M,NAP,138,223,0,Normal,169,N,Up,0 889 | 43,M,ASY,132,247,1,LVH,143,Y,Flat,1 890 | 52,M,ASY,128,204,1,Normal,156,Y,Flat,1 891 | 59,M,TA,134,204,0,Normal,162,N,Up,1 892 | 64,M,TA,170,227,0,LVH,155,N,Flat,0 893 | 66,F,NAP,146,278,0,LVH,152,N,Flat,0 894 | 39,F,NAP,138,220,0,Normal,152,N,Flat,0 895 | 57,M,ATA,154,232,0,LVH,164,N,Up,1 896 | 58,F,ASY,130,197,0,Normal,131,N,Flat,0 897 | 57,M,ASY,110,335,0,Normal,143,Y,Flat,1 898 | 47,M,NAP,130,253,0,Normal,179,N,Up,0 899 | 55,F,ASY,128,205,0,ST,130,Y,Flat,1 900 | 35,M,ATA,122,192,0,Normal,174,N,Up,0 901 | 61,M,ASY,148,203,0,Normal,161,N,Up,1 902 | 58,M,ASY,114,318,0,ST,140,N,Down,1 903 | 58,F,ASY,170,225,1,LVH,146,Y,Flat,1 904 | 58,M,ATA,125,220,0,Normal,144,N,Flat,0 905 | 56,M,ATA,130,221,0,LVH,163,N,Up,0 906 | 56,M,ATA,120,240,0,Normal,169,N,Down,0 907 | 67,M,NAP,152,212,0,LVH,150,N,Flat,1 908 | 55,F,ATA,132,342,0,Normal,166,N,Up,0 909 | 44,M,ASY,120,169,0,Normal,144,Y,Down,1 910 | 63,M,ASY,140,187,0,LVH,144,Y,Up,1 911 | 63,F,ASY,124,197,0,Normal,136,Y,Flat,1 912 | 41,M,ATA,120,157,0,Normal,182,N,Up,0 913 | 59,M,ASY,164,176,1,LVH,90,N,Flat,1 914 | 57,F,ASY,140,241,0,Normal,123,Y,Flat,1 915 | 45,M,TA,110,264,0,Normal,132,N,Flat,1 916 | 68,M,ASY,144,193,1,Normal,141,N,Flat,1 917 | 57,M,ASY,130,131,0,Normal,115,Y,Flat,1 918 | 57,F,ATA,130,236,0,LVH,174,N,Flat,1 919 | 38,M,NAP,138,175,0,Normal,173,N,Up,0 --------------------------------------------------------------------------------