{ "cells": [ { "cell_type": "code", "execution_count": 67, "id": "c28ad538", "metadata": {}, "outputs": [], "source": [ "## import required libs\n", "import pandas as pd\n", "import numpy as np\n", "import matplotlib.pyplot as plt\n", "import seaborn as sns\n", "from sklearn.model_selection import train_test_split, GridSearchCV\n", "from sklearn.preprocessing import StandardScaler\n", "from sklearn.linear_model import LogisticRegression\n", "from sklearn.tree import DecisionTreeClassifier\n", "from sklearn.ensemble import RandomForestClassifier\n", "from sklearn.svm import SVC\n", "from sklearn.feature_selection import RFE\n", "from sklearn.metrics import classification_report, confusion_matrix, accuracy_score " ] }, { "cell_type": "code", "execution_count": 68, "id": "6540f259", "metadata": { "scrolled": true }, "outputs": [ { "data": { "text/html": [ "
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HeartDiseaseBMISmokingAlcoholDrinkingStrokePhysicalHealthMentalHealthDiffWalkingSexAgeCategoryRaceDiabeticPhysicalActivityGenHealthSleepTimeAsthmaKidneyDiseaseSkinCancer
0No16.60YesNoNo3.030NoFemale55-59WhiteYesYesVery good5YesNoYes
1No20.34NoNoYes0.00NoFemale80 or olderWhiteNoYesVery good7NoNoNo
2No26.58YesNoNo20.030NoMale65-69WhiteYesYesFair8YesNoNo
3No24.21NoNoNo0.00NoFemale75-79WhiteNoNoGood6NoNoYes
4No23.71NoNoNo28.00YesFemale40-44WhiteNoYesVery good8NoNoNo
5Yes28.87YesNoNo6.00YesFemale75-79BlackNoNoFair12NoNoNo
6No21.63NoNoNo15.00NoFemale70-74WhiteNoYesFair4YesNoYes
7No31.64YesNoNo5.00YesFemale80 or olderWhiteYesNoGood9YesNoNo
8No26.45NoNoNo0.00NoFemale80 or olderWhiteNo, borderline diabetesNoFair5NoYesNo
9No40.69NoNoNo0.00YesMale65-69WhiteNoYesGood10NoNoNo
\n", "
" ], "text/plain": [ " HeartDisease BMI Smoking AlcoholDrinking Stroke PhysicalHealth \\\n", "0 No 16.60 Yes No No 3.0 \n", "1 No 20.34 No No Yes 0.0 \n", "2 No 26.58 Yes No No 20.0 \n", "3 No 24.21 No No No 0.0 \n", "4 No 23.71 No No No 28.0 \n", "5 Yes 28.87 Yes No No 6.0 \n", "6 No 21.63 No No No 15.0 \n", "7 No 31.64 Yes No No 5.0 \n", "8 No 26.45 No No No 0.0 \n", "9 No 40.69 No No No 0.0 \n", "\n", " MentalHealth DiffWalking Sex AgeCategory Race \\\n", "0 30 No Female 55-59 White \n", "1 0 No Female 80 or older White \n", "2 30 No Male 65-69 White \n", "3 0 No Female 75-79 White \n", "4 0 Yes Female 40-44 White \n", "5 0 Yes Female 75-79 Black \n", "6 0 No Female 70-74 White \n", "7 0 Yes Female 80 or older White \n", "8 0 No Female 80 or older White \n", "9 0 Yes Male 65-69 White \n", "\n", " Diabetic PhysicalActivity GenHealth SleepTime Asthma \\\n", "0 Yes Yes Very good 5 Yes \n", "1 No Yes Very good 7 No \n", "2 Yes Yes Fair 8 Yes \n", "3 No No Good 6 No \n", "4 No Yes Very good 8 No \n", "5 No No Fair 12 No \n", "6 No Yes Fair 4 Yes \n", "7 Yes No Good 9 Yes \n", "8 No, borderline diabetes No Fair 5 No \n", "9 No Yes Good 10 No \n", "\n", " KidneyDisease SkinCancer \n", "0 No Yes \n", "1 No No \n", "2 No No \n", "3 No Yes \n", "4 No No \n", "5 No No \n", "6 No Yes \n", "7 No No \n", "8 Yes No \n", "9 No No " ] }, "execution_count": 68, "metadata": {}, "output_type": "execute_result" } ], "source": [ "## load dataset\n", "## https://www.kaggle.com/datasets/abubakarsiddiquemahi/heart-disease-dataset\n", "\n", "data = pd.read_excel('Heart Disease_4.xlsx')\n", "\n", "## show\n", "data.head(10)\n", " " ] }, { "cell_type": "code", "execution_count": 69, "id": "cc9fc37d-b301-4078-badd-a7ef3912f59f", "metadata": { "scrolled": true }, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HeartDisease 0\n", "BMI 0\n", "Smoking 0\n", "AlcoholDrinking 0\n", "Stroke 3\n", "PhysicalHealth 3\n", "MentalHealth 0\n", "DiffWalking 0\n", "Sex 0\n", "AgeCategory 0\n", "Race 0\n", "Diabetic 0\n", "PhysicalActivity 0\n", "GenHealth 0\n", "SleepTime 0\n", "Asthma 0\n", "KidneyDisease 0\n", "SkinCancer 0\n", "dtype: int64\n" ] } ], "source": [ "## check missing values\n", "print(data.isnull().sum())" ] }, { "cell_type": "code", "execution_count": 70, "id": "c0e852f3", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "HeartDisease 0\n", "BMI 0\n", "Smoking 0\n", "AlcoholDrinking 0\n", "Stroke 0\n", "PhysicalHealth 0\n", "MentalHealth 0\n", "DiffWalking 0\n", "Sex 0\n", "AgeCategory 0\n", "Race 0\n", "Diabetic 0\n", "PhysicalActivity 0\n", "GenHealth 0\n", "SleepTime 0\n", "Asthma 0\n", "KidneyDisease 0\n", "SkinCancer 0\n", "dtype: int64\n" ] } ], "source": [ "## fill missing values:\n", "numeric_cols = data.select_dtypes(include='number').columns\n", "data[numeric_cols] = data[numeric_cols].fillna(data[numeric_cols].mean())\n", "data['Stroke'] = data['Stroke'].fillna(data['Stroke'].mode()[0])\n", "\n", "## check missing values\n", "print(data.isnull().sum())" ] }, { "cell_type": "code", "execution_count": 71, "id": "b083709c-1f29-42a9-9a28-09e2a2e42745", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ " BMI PhysicalHealth MentalHealth SleepTime\n", "count 9999.000000 9999.000000 9999.000000 9999.000000\n", "mean 28.630799 3.892557 3.781278 7.127313\n", "std 6.454691 8.381689 7.909752 1.517746\n", "min 12.480000 0.000000 0.000000 1.000000\n", "25% 24.210000 0.000000 0.000000 6.000000\n", "50% 27.460000 0.000000 0.000000 7.000000\n", "75% 31.930000 2.000000 3.000000 8.000000\n", "max 83.000000 30.000000 30.000000 20.000000\n" ] } ], "source": [ "## show\n", "print(data.describe())" ] }, { "cell_type": "code", "execution_count": 72, "id": "f2640fee", "metadata": {}, "outputs": [], "source": [ "## split data to train & test\n", "\n", "X = data.drop('HeartDisease', axis=1)\n", "y = data['HeartDisease']\n", "\n", "categorical_cols = X.select_dtypes(include='object').columns\n", "X_encoded = pd.get_dummies(X, columns=categorical_cols, drop_first=True)\n", "X_train, X_test, y_train, y_test = train_test_split(X_encoded, y, test_size=0.2, random_state=42, stratify=y)\n", "\n", "numeric_cols = X.select_dtypes(include='number').columns\n", "encoded_numeric_cols = [col for col in X_encoded.columns if col in numeric_cols]\n", "\n", "scaler = StandardScaler()\n", "X_train_scaled = X_train.copy()\n", "X_test_scaled = X_test.copy()\n", "X_train_scaled[encoded_numeric_cols] = scaler.fit_transform(X_train[encoded_numeric_cols])\n", "X_test_scaled[encoded_numeric_cols] = scaler.transform(X_test[encoded_numeric_cols]) " ] }, { "cell_type": "code", "execution_count": 73, "id": "49d4f3de-27b3-4a75-b17c-043325db0b57", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "X_train shape: (7999, 37)\n", "y_train shape: (7999,)\n", "X_test shape : (2000, 37)\n", "y_test shape : (2000,)\n" ] } ], "source": [ "# view\n", "print(\"X_train shape:\", X_train.shape)\n", "print(\"y_train shape:\", y_train.shape)\n", "\n", "print(\"X_test shape :\", X_test.shape)\n", "print(\"y_test shape :\", y_test.shape)" ] }, { "cell_type": "code", "execution_count": 74, "id": "bfbb5e29", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "tree-based: ['MentalHealth', 'SleepTime', 'PhysicalHealth', 'BMI']\n", "rfe: ['BMI', 'PhysicalHealth', 'MentalHealth', 'SleepTime']\n" ] } ], "source": [ "# Apply feature selection\n", "\n", "numeric_cols = X_train.select_dtypes(include='number').columns\n", "\n", "scaler = StandardScaler()\n", "X_train_num = pd.DataFrame(scaler.fit_transform(X_train[numeric_cols]), columns=numeric_cols)\n", "X_test_num = pd.DataFrame(scaler.transform(X_test[numeric_cols]), columns=numeric_cols)\n", "\n", "rf_model = RandomForestClassifier(random_state=42)\n", "rf_model.fit(X_train_num, y_train)\n", "\n", "importances = rf_model.feature_importances_\n", "top_rf_indices = np.argsort(importances)[-4:]\n", "selected_rf_features = X_train_num.columns[top_rf_indices]\n", "print(\"tree-based:\", selected_rf_features.tolist())\n", "\n", "lr_model = LogisticRegression(max_iter=300, random_state=42)\n", "rfe = RFE(lr_model, n_features_to_select=4)\n", "rfe.fit(X_train_num, y_train)\n", "\n", "selected_rfe_features = X_train_num.columns[rfe.support_]\n", "print(\"rfe:\", selected_rfe_features.tolist())\n" ] }, { "cell_type": "code", "execution_count": 75, "id": "62cb0fd1", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "Logistic Regression:\n", " precision recall f1-score support\n", "\n", " No 0.91 0.99 0.95 1803\n", " Yes 0.52 0.13 0.20 197\n", "\n", " accuracy 0.90 2000\n", " macro avg 0.72 0.56 0.58 2000\n", "weighted avg 0.87 0.90 0.87 2000\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Decision Tree:\n", " precision recall f1-score support\n", "\n", " No 0.92 0.90 0.91 1803\n", " Yes 0.23 0.27 0.25 197\n", "\n", " accuracy 0.84 2000\n", " macro avg 0.57 0.58 0.58 2000\n", "weighted avg 0.85 0.84 0.84 2000\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Random Forest:\n", " precision recall f1-score support\n", "\n", " No 0.91 0.99 0.95 1803\n", " Yes 0.39 0.08 0.13 197\n", "\n", " accuracy 0.90 2000\n", " macro avg 0.65 0.53 0.54 2000\n", "weighted avg 0.86 0.90 0.86 2000\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "Support Vector Machine:\n", " precision recall f1-score support\n", "\n", " No 0.90 1.00 0.95 1803\n", " Yes 0.33 0.02 0.03 197\n", "\n", " accuracy 0.90 2000\n", " macro avg 0.62 0.51 0.49 2000\n", "weighted avg 0.85 0.90 0.86 2000\n", "\n" ] }, { "data": { "image/png": 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", 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" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## apply models on all features\n", "\n", "def evaluate_class(model, X_test, y_test):\n", " y_pred = model.predict(X_test)\n", " print(classification_report(y_test, y_pred))\n", " cm = confusion_matrix(y_test, y_pred)\n", " sns.heatmap(cm, annot=True, fmt='d', cmap='Blues')\n", " plt.xlabel('Predicted')\n", " plt.ylabel('Actual')\n", " plt.show()\n", "\n", "# Logistic Regression\n", "log_model = LogisticRegression(max_iter=300)\n", "log_model.fit(X_train_scaled, y_train)\n", "print(\"Logistic Regression:\")\n", "evaluate_class(log_model, X_test_scaled, y_test)\n", "\n", "# Decision Tree Classifier\n", "dt_model = DecisionTreeClassifier()\n", "dt_model.fit(X_train_scaled, y_train)\n", "print(\"Decision Tree:\")\n", "evaluate_class(dt_model, X_test_scaled, y_test)\n", "\n", "# Random Forest Classifier\n", "rf_model = RandomForestClassifier()\n", "rf_model.fit(X_train_scaled, y_train)\n", "print(\"Random Forest:\")\n", "evaluate_class(rf_model, X_test_scaled, y_test)\n", "\n", "# Support Vector Machine\n", "svm_model = SVC()\n", "svm_model.fit(X_train_scaled, y_train)\n", "print(\"Support Vector Machine:\")\n", "evaluate_class(svm_model, X_test_scaled, y_test)" ] }, { "cell_type": "code", "execution_count": 76, "id": "c08e5c46", "metadata": {}, "outputs": [ { "name": "stdout", "output_type": "stream", "text": [ "randome forest - tree-based selected features:\n", " precision recall f1-score support\n", "\n", " No 0.90 0.96 0.93 1803\n", " Yes 0.11 0.05 0.07 197\n", "\n", " accuracy 0.87 2000\n", " macro avg 0.51 0.50 0.50 2000\n", "weighted avg 0.82 0.87 0.85 2000\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" }, { "name": "stdout", "output_type": "stream", "text": [ "randome forest - rfe selected features:\n", " precision recall f1-score support\n", "\n", " No 0.90 0.96 0.93 1803\n", " Yes 0.09 0.04 0.05 197\n", "\n", " accuracy 0.87 2000\n", " macro avg 0.50 0.50 0.49 2000\n", "weighted avg 0.82 0.87 0.84 2000\n", "\n" ] }, { "data": { "image/png": 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", "text/plain": [ "
" ] }, "metadata": {}, "output_type": "display_data" } ], "source": [ "## apply models on selected features\n", "\n", "# tree-based selected features\n", "X_train_rf = X_train[selected_rf_features]\n", "X_test_rf = X_test[selected_rf_features]\n", "\n", "scaler_rf = StandardScaler()\n", "X_train_rf_scaled = scaler_rf.fit_transform(X_train_rf)\n", "X_test_rf_scaled = scaler_rf.transform(X_test_rf)\n", "\n", "rf_model_selected = RandomForestClassifier()\n", "rf_model_selected.fit(X_train_rf_scaled, y_train)\n", "\n", "print(\"randome forest - tree-based selected features:\")\n", "evaluate_class(rf_model_selected, X_test_rf_scaled, y_test)\n", "\n", "# rfe selected features\n", "X_train_rfe = X_train[selected_rf_features]\n", "X_test_rfe = X_test[selected_rf_features]\n", "\n", "scaler_rfe = StandardScaler()\n", "X_train_rfe_scaled = scaler_rfe.fit_transform(X_train_rfe)\n", "X_test_rfe_scaled = scaler_rfe.transform(X_test_rfe)\n", "\n", "rf_model_rfe_selected = RandomForestClassifier()\n", "rf_model_rfe_selected.fit(X_train_rfe_scaled, y_train)\n", "\n", "print(\"randome forest - rfe selected features:\")\n", "evaluate_class(rf_model_rfe_selected, X_test_rfe_scaled, y_test)\n", "\n", " " ] }, { "cell_type": "code", "execution_count": 77, "id": "5dda1428", "metadata": {}, "outputs": [], "source": [ "\n", "## save\n", "data.to_csv('Heart Disease V2.csv')\n", " " ] }, { "cell_type": "code", "execution_count": null, "id": "20fe6913-08e8-483f-abfd-b4ceba04d1d8", "metadata": {}, "outputs": [], "source": [] } ], "metadata": { "kernelspec": { "display_name": "Python 3 (ipykernel)", "language": "python", "name": "python3" }, "language_info": { "codemirror_mode": { "name": "ipython", "version": 3 }, "file_extension": ".py", "mimetype": "text/x-python", "name": "python", "nbconvert_exporter": "python", "pygments_lexer": "ipython3", "version": "3.12.7" } }, "nbformat": 4, "nbformat_minor": 5 }