commit 7746f998d6b443e247ca1f3744f707ad8e118116 Author: psavarmattas Date: Wed Feb 8 20:46:56 2023 +0530 First Commit diff --git a/Cardiovascular-prediction-model.joblib b/Cardiovascular-prediction-model.joblib new file mode 100644 index 0000000..5caed9c Binary files /dev/null and b/Cardiovascular-prediction-model.joblib differ diff --git a/README.MD b/README.MD new file mode 100644 index 0000000..97388a4 --- /dev/null +++ b/README.MD @@ -0,0 +1,129 @@ +# Cardiovascular Heart Disease Prediction Model (Stacking Model) +`Last Updated: 08th February 2023` + +### **THIS RESEARCH WILL BE PUBLISHED** + +## Dataset Information + +### About this dataset: +This dataset contains detailed information on the risk factors for cardiovascular disease. It includes information on age, gender, height, weight, blood pressure values, cholesterol levels, glucose levels, smoking habits and alcohol consumption of over 70 thousand individuals. Additionally it outlines if the person is active or not and if he or she has any cardiovascular diseases. This dataset provides a great resource for researchers to apply modern machine learning techniques to explore the potential relations between risk factors and cardiovascular disease that can ultimately lead to improved understanding of this serious health issue and design better preventive measures + +### Aim: +This dataset can be used to explore the risk factors of cardiovascular disease in adults. The aim is to understand how certain demographic factors, health behaviors and biological markers affect the development of heart disease. + +### Dataset Description: This dataset contains 70,000 records of patients with the following 13 features: + +1. Age: Age of participant (integer) +2. Gender: Gender of participant (male/female). +3. Height: Height measured in centimeters (integer) +4. Weight: Weight measured in kilograms (integer) +5. Ap_hi: Systolic blood pressure reading taken from patient (integer) +6. Ap_lo : Diastolic blood pressure reading taken from patient (integer) +7. Cholesterol : Total cholesterol level read as mg/dl on a scale 0 - 5+ units( integer). Each unit denoting increase/decrease by 20 mg/dL respectively. +8. Gluc : Glucose level read as mmol/l on a scale 0 - 16+ units( integer). Each unit denoting increase Decreaseby 1 mmol/L respectively. +9. Smoke : Whether person smokes or not(binary; 0= No , 1=Yes). +10. Alco ​­ : Whether person drinks alcohol or not(binary; 0 =No ,1 =Yes ). +11. Active : whether person physically active or not( Binary ;0 =No,1 = Yes ). +12. Cardio ­­ : whether person suffers from cardiovascular diseases or not(Binary ;0 – no , 1 ­‑yes ). + +## Basic Outline For A Machine Learning Model: + +Points to keep in mind when working with a machine learning model + +1. Import the Data +2. Clean the Data +3. Split the Data into Training/Test Sets +4. Create Model +5. Train the Model +6. Make Predictions +7. Evaluate and Improve + +## Libraries Needed: + +1. Numpy +2. Pandas +3. Matplotlib +4. Scikit-Learn +5. Seaborn +6. Cufflinks + +## Tools Needed: + +1. Jupyter (IDE) +2. Github (To pull repository) + +## Installation Instructions: + +1. Clone the repository. +2. Open terminal and 'cd' to the directory where to saved the repository. +3. Run the command `pip install -r requirements.txt`. +4. Now start working on the repository. + +## Methodology: + +1. IMPORTING LIBRARIES AND LOADING DATA +2. DATA EXPLORATION +3. DATA PREPROCESSING + + a. Target and Feature values / Train Test Split +4. MODEL BUILDING + + a. KNeighborsClassifier + + b. Support Vector Classification (SVC) + + c. Decision Tree Classifier + + d. Random Forest Classifier + + e. Multi-layer Perceptron classifier + + f. Stacking Classifier + +5. Prediction Testing + + +## In-Depth Working of this model: + +### Imports + +1. Numpy: For working with arrays. +2. Pandas: Used to analyze the data. +3. OS Module: For working with files/directories. +4. matplotlib: Used for programmatic plot generation. +5. Seaborn: Used for statistical graphics. +6. Warnings: Used to control warnings in Python. +7. sklearn: These are simple and efficient tools for predictive data analsis. + +### Data Exploration + +1. pd.read_csv: Used to load cvs files in pandas dataframe. +2. df.head: It returns first 'n' rows. +3. pd.info: It prints information about the dataframe. +4. df.describe: It generates descriptive statistics. +5. unique: It returns unique values from the dataframe. + +### Data Preprocessing + +1. df.isnull: It detects missing values. +2. df.drop: It drops speficied labels from rows and columns. +3. get_dummies: It converts categorial variable into dummy/indicator variable. +4. df.dropna: It drops coloumns and rows where null value is present. + +### Model Building + +1. KNeighborsClassifier: Classifier implementing the k-nearest neighbors vote. +2. Support Vector Classification (SVC): SVC is class capable of performing binary and multi-class classification on a dataset. +3. Decision Tree Classifier: Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation. +4. Random Forest Classifier: A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree. +5. Multi-layer Perceptron classifier: This model optimizes the log-loss function using LBFGS or stochastic gradient descent. +6. Stacking Classifier: Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator. +7.. joblib.dump: It collects all the learning and dumps it in one file. +8. joblib.load: It reconstructs the file for use which is created by 'dump' method. + +## Prediction Testing + +1. random: It generates random output. + +## Acknowledgement +This data was possible because of the work of [Kuzak Dempsy](https://data.world/kudem). \ No newline at end of file diff --git a/datasetCleaned.csv b/datasetCleaned.csv new file mode 100644 index 0000000..2ee6a1b --- /dev/null +++ b/datasetCleaned.csv @@ -0,0 +1,70001 @@ +,age,gender,height,weight,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active,cardio +0,18393,2,168,62.0,110,80,1,1,0,0,1,0 +1,20228,1,156,85.0,140,90,3,1,0,0,1,1 +2,18857,1,165,64.0,130,70,3,1,0,0,0,1 +3,17623,2,169,82.0,150,100,1,1,0,0,1,1 +4,17474,1,156,56.0,100,60,1,1,0,0,0,0 +5,21914,1,151,67.0,120,80,2,2,0,0,0,0 +6,22113,1,157,93.0,130,80,3,1,0,0,1,0 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It includes information on age, gender, height, weight, blood pressure values, cholesterol levels, glucose levels, smoking habits and alcohol consumption of over 70 thousand individuals. Additionally it outlines if the person is active or not and if he or she has any cardiovascular diseases. This dataset provides a great resource for researchers to apply modern machine learning techniques to explore the potential relations between risk factors and cardiovascular disease that can ultimately lead to improved understanding of this serious health issue and design better preventive measures\n", + "\n", + "### Aim:\n", + "This dataset can be used to explore the risk factors of cardiovascular disease in adults. The aim is to understand how certain demographic factors, health behaviors and biological markers affect the development of heart disease.\n", + "\n", + "### Dataset Description: This dataset contains 70,000 records of patients with the following 13 features:\n", + "\n", + "1. Age: Age of participant (integer)\n", + "2. Gender: Gender of participant (male/female).\n", + "3. Height: Height measured in centimeters (integer)\n", + "4. Weight: Weight measured in kilograms (integer)\n", + "5. Ap_hi: Systolic blood pressure reading taken from patient (integer)\n", + "6. Ap_lo : Diastolic blood pressure reading taken from patient (integer)\n", + "7. Cholesterol : Total cholesterol level read as mg/dl on a scale 0 - 5+ units( integer). Each unit denoting increase/decrease by 20 mg/dL respectively.\n", + "8. Gluc : Glucose level read as mmol/l on a scale 0 - 16+ units( integer). Each unit denoting increase Decreaseby 1 mmol/L respectively.\n", + "9. Smoke : Whether person smokes or not(binary; 0= No , 1=Yes).\n", + "10. Alco ​­ : Whether person drinks alcohol or not(binary; 0 =No ,1 =Yes ).\n", + "11. Active : whether person physically active or not( Binary ;0 =No,1 = Yes ).\n", + "12. Cardio ­­ : whether person suffers from cardiovascular diseases or not(Binary ;0 – no , 1 ­‑yes )." + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## IMPORTING LIBRARIES AND LOADING DATA" + ] + }, + { + "cell_type": "code", + "execution_count": 2, + "metadata": {}, + "outputs": [], + "source": [ + "import numpy as np \n", + "import pandas as pd \n", + "import os\n", + "import matplotlib.pyplot as plt\n", + "import seaborn as sns\n", + "\n", + "import warnings\n", + "warnings.filterwarnings('ignore')\n", + "\n", + "from sklearn.model_selection import train_test_split\n", + "from sklearn.metrics import accuracy_score\n", + "from sklearn.metrics import matthews_corrcoef\n", + "from sklearn.metrics import f1_score\n", + "\n", + "from sklearn.neighbors import KNeighborsClassifier\n", + "from sklearn.svm import LinearSVC\n", + "from sklearn.ensemble import RandomForestClassifier\n", + "from sklearn.tree import DecisionTreeClassifier\n", + "from sklearn.neural_network import MLPClassifier\n", + "from sklearn.ensemble import StackingClassifier\n", + "from sklearn.linear_model import LogisticRegression\n", + "\n", + "import joblib as joblib" + ] + }, + { + "cell_type": "code", + "execution_count": 3, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " index id age gender height weight ap_hi ap_lo cholesterol gluc \\\n", + "0 0 0 18393 2 168 62.0 110 80 1 1 \n", + "1 1 1 20228 1 156 85.0 140 90 3 1 \n", + "2 2 2 18857 1 165 64.0 130 70 3 1 \n", + "3 3 3 17623 2 169 82.0 150 100 1 1 \n", + "4 4 4 17474 1 156 56.0 100 60 1 1 \n", + "5 5 8 21914 1 151 67.0 120 80 2 2 \n", + "6 6 9 22113 1 157 93.0 130 80 3 1 \n", + "7 7 12 22584 2 178 95.0 130 90 3 3 \n", + "8 8 13 17668 1 158 71.0 110 70 1 1 \n", + "9 9 14 19834 1 164 68.0 110 60 1 1 \n", + "\n", + " smoke alco active cardio \n", + "0 0 0 1 0 \n", + "1 0 0 1 1 \n", + "2 0 0 0 1 \n", + "3 0 0 1 1 \n", + "4 0 0 0 0 \n", + "5 0 0 0 0 \n", + "6 0 0 1 0 \n", + "7 0 0 1 1 \n", + "8 0 0 1 0 \n", + "9 0 0 0 0 " + ] + }, + "execution_count": 3, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df = pd.read_csv('heart_data.csv')\n", + "df.head(10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DATA EXPLORATION" + ] + }, + { + "cell_type": "code", + "execution_count": 4, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "\n", + "RangeIndex: 70000 entries, 0 to 69999\n", + "Data columns (total 14 columns):\n", + " # Column Non-Null Count Dtype \n", + "--- ------ -------------- ----- \n", + " 0 index 70000 non-null int64 \n", + " 1 id 70000 non-null int64 \n", + " 2 age 70000 non-null int64 \n", + " 3 gender 70000 non-null int64 \n", + " 4 height 70000 non-null int64 \n", + " 5 weight 70000 non-null float64\n", + " 6 ap_hi 70000 non-null int64 \n", + " 7 ap_lo 70000 non-null int64 \n", + " 8 cholesterol 70000 non-null int64 \n", + " 9 gluc 70000 non-null int64 \n", + " 10 smoke 70000 non-null int64 \n", + " 11 alco 70000 non-null int64 \n", + " 12 active 70000 non-null int64 \n", + " 13 cardio 70000 non-null int64 \n", + "dtypes: float64(1), int64(13)\n", + "memory usage: 7.5 MB\n" + ] + } + ], + "source": [ + "df.info()" + ] + }, + { + "cell_type": "code", + "execution_count": 5, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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indexidagegenderheightweightap_hiap_locholesterolglucsmokealcoactivecardio
count70000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.00000070000.000000
mean34999.50000049972.41990019468.8658141.349571164.35922974.205690128.81728696.6304141.3668711.2264570.0881290.0537710.8037290.499700
std20207.40375928851.3023232467.2516670.4768388.21012614.395757154.011419188.4725300.6802500.5722700.2834840.2255680.3971790.500003
min0.0000000.00000010798.0000001.00000055.00000010.000000-150.000000-70.0000001.0000001.0000000.0000000.0000000.0000000.000000
25%17499.75000025006.75000017664.0000001.000000159.00000065.000000120.00000080.0000001.0000001.0000000.0000000.0000001.0000000.000000
50%34999.50000050001.50000019703.0000001.000000165.00000072.000000120.00000080.0000001.0000001.0000000.0000000.0000001.0000000.000000
75%52499.25000074889.25000021327.0000002.000000170.00000082.000000140.00000090.0000002.0000001.0000000.0000000.0000001.0000001.000000
max69999.00000099999.00000023713.0000002.000000250.000000200.00000016020.00000011000.0000003.0000003.0000001.0000001.0000001.0000001.000000
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" + ], + "text/plain": [ + " index id age gender height \\\n", + "count 70000.000000 70000.000000 70000.000000 70000.000000 70000.000000 \n", + "mean 34999.500000 49972.419900 19468.865814 1.349571 164.359229 \n", + "std 20207.403759 28851.302323 2467.251667 0.476838 8.210126 \n", + "min 0.000000 0.000000 10798.000000 1.000000 55.000000 \n", + "25% 17499.750000 25006.750000 17664.000000 1.000000 159.000000 \n", + "50% 34999.500000 50001.500000 19703.000000 1.000000 165.000000 \n", + "75% 52499.250000 74889.250000 21327.000000 2.000000 170.000000 \n", + "max 69999.000000 99999.000000 23713.000000 2.000000 250.000000 \n", + "\n", + " weight ap_hi ap_lo cholesterol gluc \\\n", + "count 70000.000000 70000.000000 70000.000000 70000.000000 70000.000000 \n", + "mean 74.205690 128.817286 96.630414 1.366871 1.226457 \n", + "std 14.395757 154.011419 188.472530 0.680250 0.572270 \n", + "min 10.000000 -150.000000 -70.000000 1.000000 1.000000 \n", + "25% 65.000000 120.000000 80.000000 1.000000 1.000000 \n", + "50% 72.000000 120.000000 80.000000 1.000000 1.000000 \n", + "75% 82.000000 140.000000 90.000000 2.000000 1.000000 \n", + "max 200.000000 16020.000000 11000.000000 3.000000 3.000000 \n", + "\n", + " smoke alco active cardio \n", + "count 70000.000000 70000.000000 70000.000000 70000.000000 \n", + "mean 0.088129 0.053771 0.803729 0.499700 \n", + "std 0.283484 0.225568 0.397179 0.500003 \n", + "min 0.000000 0.000000 0.000000 0.000000 \n", + "25% 0.000000 0.000000 1.000000 0.000000 \n", + "50% 0.000000 0.000000 1.000000 0.000000 \n", + "75% 0.000000 0.000000 1.000000 1.000000 \n", + "max 1.000000 1.000000 1.000000 1.000000 " + ] + }, + "execution_count": 5, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.describe()" + ] + }, + { + "cell_type": "code", + "execution_count": 6, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "index 0\n", + "id 0\n", + "age 0\n", + "gender 0\n", + "height 0\n", + "weight 0\n", + "ap_hi 0\n", + "ap_lo 0\n", + "cholesterol 0\n", + "gluc 0\n", + "smoke 0\n", + "alco 0\n", + "active 0\n", + "cardio 0\n", + "dtype: int64" + ] + }, + "execution_count": 6, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "df.isnull().sum()" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## DATA PREPROCESSING" + ] + }, + { + "cell_type": "code", + "execution_count": 7, + "metadata": {}, + "outputs": [], + "source": [ + "df = df.drop(['index','id'], axis = 1)" + ] + }, + { + "cell_type": "code", + "execution_count": 8, + "metadata": {}, + "outputs": [], + "source": [ + "df.to_csv('datasetCleaned.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 9, + "metadata": {}, + "outputs": [], + "source": [ + "X = df.drop(['cardio'], axis = 1)\n", + "y = df['cardio']" + ] + }, + { + "cell_type": "code", + "execution_count": 10, + "metadata": {}, + "outputs": [], + "source": [ + "X.to_csv('splitX.csv')\n", + "y.to_csv('splity.csv')" + ] + }, + { + "cell_type": "code", + "execution_count": 11, + "metadata": {}, + "outputs": [ + { + "data": { + "text/html": [ + "
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" + ], + "text/plain": [ + " age gender height weight ap_hi ap_lo cholesterol gluc smoke \\\n", + "0 18393 2 168 62.0 110 80 1 1 0 \n", + "1 20228 1 156 85.0 140 90 3 1 0 \n", + "2 18857 1 165 64.0 130 70 3 1 0 \n", + "3 17623 2 169 82.0 150 100 1 1 0 \n", + "4 17474 1 156 56.0 100 60 1 1 0 \n", + "5 21914 1 151 67.0 120 80 2 2 0 \n", + "6 22113 1 157 93.0 130 80 3 1 0 \n", + "7 22584 2 178 95.0 130 90 3 3 0 \n", + "8 17668 1 158 71.0 110 70 1 1 0 \n", + "9 19834 1 164 68.0 110 60 1 1 0 \n", + "\n", + " alco active \n", + "0 0 1 \n", + "1 0 1 \n", + "2 0 0 \n", + "3 0 1 \n", + "4 0 0 \n", + "5 0 0 \n", + "6 0 1 \n", + "7 0 1 \n", + "8 0 1 \n", + "9 0 0 " + ] + }, + "execution_count": 11, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X.head(10)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Target and Feature values / Train Test Split" + ] + }, + { + "cell_type": "code", + "execution_count": 12, + "metadata": {}, + "outputs": [ + { + "data": { + "text/plain": [ + "((56000, 11), (14000, 11))" + ] + }, + "execution_count": 12, + "metadata": {}, + "output_type": "execute_result" + } + ], + "source": [ + "X_train, X_test, y_train , y_test = train_test_split(X,y, test_size = 0.2, random_state = 42)\n", + "X_train.shape, X_test.shape" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## MODEL BUILDING" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### KNeighborsClassifier\n", + "\n", + "Classifier implementing the k-nearest neighbors vote.\n", + "\n", + "class sklearn.neighbors.KNeighborsClassifier(n_neighbors=5, *, weights='uniform', algorithm='auto', leaf_size=30, p=2, metric='minkowski', metric_params=None, n_jobs=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 13, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.8160535714285714\n", + "- MCC: 0.6321696476040224\n", + "- F1 score: 0.8160418618700654\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.6652142857142858\n", + "- MCC: 0.33048635555172207\n", + "- F1 score: 0.6651977900484768\n" + ] + } + ], + "source": [ + "knn = KNeighborsClassifier(3) # Define classifier\n", + "knn.fit(X_train, y_train) # Train model\n", + "\n", + "# Make predictions\n", + "y_train_pred = knn.predict(X_train)\n", + "y_test_pred = knn.predict(X_test)\n", + "\n", + "# Training set performance\n", + "knn_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "knn_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "knn_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set performance\n", + "knn_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "knn_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "knn_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % knn_train_accuracy)\n", + "print('- MCC: %s' % knn_train_mcc)\n", + "print('- F1 score: %s' % knn_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % knn_test_accuracy)\n", + "print('- MCC: %s' % knn_test_mcc)\n", + "print('- F1 score: %s' % knn_test_f1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Support Vector Classification (LinearSVC)\n", + "\n", + "Linear Support Vector Classification.\n", + "\n", + "Similar to SVC with parameter kernel=’linear’, but implemented in terms of liblinear rather than libsvm, so it has more flexibility in the choice of penalties and loss functions and should scale better to large numbers of samples.\n", + "\n", + "This class supports both dense and sparse input and the multiclass support is handled according to a one-vs-the-rest scheme.\n", + "\n", + "class sklearn.svm.LinearSVC(penalty='l2', loss='squared_hinge', *, dual=True, tol=0.0001, C=1.0, multi_class='ovr', fit_intercept=True, intercept_scaling=1, class_weight=None, verbose=0, random_state=None, max_iter=1000)" + ] + }, + { + "cell_type": "code", + "execution_count": 14, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.5605178571428572\n", + "- MCC: 0.1982763778835479\n", + "- F1 score: 0.4796980585847672\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.5639285714285714\n", + "- MCC: 0.20614681188435108\n", + "- F1 score: 0.4833516841135736\n" + ] + } + ], + "source": [ + "svm_rbf = LinearSVC(C=1)\n", + "svm_rbf.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_train_pred = svm_rbf.predict(X_train)\n", + "y_test_pred = svm_rbf.predict(X_test)\n", + "\n", + "# Training set performance\n", + "svm_rbf_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "svm_rbf_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "svm_rbf_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set performance\n", + "svm_rbf_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "svm_rbf_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "svm_rbf_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % svm_rbf_train_accuracy)\n", + "print('- MCC: %s' % svm_rbf_train_mcc)\n", + "print('- F1 score: %s' % svm_rbf_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % svm_rbf_test_accuracy)\n", + "print('- MCC: %s' % svm_rbf_test_mcc)\n", + "print('- F1 score: %s' % svm_rbf_test_f1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Decision Tree Classifier\n", + "\n", + "A decision tree classifier.\n", + "Decision Trees (DTs) are a non-parametric supervised learning method used for classification and regression. The goal is to create a model that predicts the value of a target variable by learning simple decision rules inferred from the data features. A tree can be seen as a piecewise constant approximation.\n", + "\n", + "class sklearn.tree.DecisionTreeClassifier(*, criterion='gini', splitter='best', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features=None, random_state=None, max_leaf_nodes=None, min_impurity_decrease=0.0, class_weight=None, ccp_alpha=0.0)" + ] + }, + { + "cell_type": "code", + "execution_count": 15, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.7315178571428571\n", + "- MCC: 0.4659467768577868\n", + "- F1 score: 0.7306405474693207\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.7345714285714285\n", + "- MCC: 0.4719694304599963\n", + "- F1 score: 0.7338280385263793\n" + ] + } + ], + "source": [ + "dt = DecisionTreeClassifier(max_depth=5) # Define classifier\n", + "dt.fit(X_train, y_train) # Train model\n", + "\n", + "# Make predictions\n", + "y_train_pred = dt.predict(X_train)\n", + "y_test_pred = dt.predict(X_test)\n", + "\n", + "# Training set performance\n", + "dt_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "dt_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "dt_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set performance\n", + "dt_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "dt_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "dt_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % dt_train_accuracy)\n", + "print('- MCC: %s' % dt_train_mcc)\n", + "print('- F1 score: %s' % dt_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % dt_test_accuracy)\n", + "print('- MCC: %s' % dt_test_mcc)\n", + "print('- F1 score: %s' % dt_test_f1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Random Forest Classifier\n", + "\n", + "A random forest classifier.\n", + "\n", + "A random forest is a meta estimator that fits a number of decision tree classifiers on various sub-samples of the dataset and uses averaging to improve the predictive accuracy and control over-fitting. The sub-sample size is controlled with the max_samples parameter if bootstrap=True (default), otherwise the whole dataset is used to build each tree.\n", + "\n", + "class sklearn.ensemble.RandomForestClassifier(n_estimators=100, *, criterion='gini', max_depth=None, min_samples_split=2, min_samples_leaf=1, min_weight_fraction_leaf=0.0, max_features='sqrt', max_leaf_nodes=None, min_impurity_decrease=0.0, bootstrap=True, oob_score=False, n_jobs=None, random_state=None, verbose=0, warm_start=False, class_weight=None, ccp_alpha=0.0, max_samples=None)" + ] + }, + { + "cell_type": "code", + "execution_count": 16, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.9791964285714285\n", + "- MCC: 0.9587219000239561\n", + "- F1 score: 0.9791925222267412\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.6987142857142857\n", + "- MCC: 0.3992035028287897\n", + "- F1 score: 0.6981032127638641\n" + ] + } + ], + "source": [ + "rf = RandomForestClassifier(n_estimators=10) # Define classifier\n", + "rf.fit(X_train, y_train) # Train model\n", + "\n", + "# Make predictions\n", + "y_train_pred = rf.predict(X_train)\n", + "y_test_pred = rf.predict(X_test)\n", + "\n", + "# Training set performance\n", + "rf_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "rf_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "rf_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set performance\n", + "rf_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "rf_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "rf_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % rf_train_accuracy)\n", + "print('- MCC: %s' % rf_train_mcc)\n", + "print('- F1 score: %s' % rf_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % rf_test_accuracy)\n", + "print('- MCC: %s' % rf_test_mcc)\n", + "print('- F1 score: %s' % rf_test_f1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Multi-layer Perceptron classifier\n", + "\n", + "This model optimizes the log-loss function using LBFGS or stochastic gradient descent.\n", + "\n", + "class sklearn.neural_network.MLPClassifier(hidden_layer_sizes=(100,), activation='relu', *, solver='adam', alpha=0.0001, batch_size='auto', learning_rate='constant', learning_rate_init=0.001, power_t=0.5, max_iter=200, shuffle=True, random_state=None, tol=0.0001, verbose=False, warm_start=False, momentum=0.9, nesterovs_momentum=True, early_stopping=False, validation_fraction=0.1, beta_1=0.9, beta_2=0.999, epsilon=1e-08, n_iter_no_change=10, max_fun=15000)" + ] + }, + { + "cell_type": "code", + "execution_count": 17, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.687125\n", + "- MCC: 0.4003987827689168\n", + "- F1 score: 0.676753367823394\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.6843571428571429\n", + "- MCC: 0.3954742763523196\n", + "- F1 score: 0.6739382133165988\n" + ] + } + ], + "source": [ + "mlp = MLPClassifier(alpha=1, max_iter=1000)\n", + "mlp.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_train_pred = mlp.predict(X_train)\n", + "y_test_pred = mlp.predict(X_test)\n", + "\n", + "# Training set performance\n", + "mlp_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "mlp_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "mlp_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set performance\n", + "mlp_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "mlp_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "mlp_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % mlp_train_accuracy)\n", + "print('- MCC: %s' % mlp_train_mcc)\n", + "print('- F1 score: %s' % mlp_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % mlp_test_accuracy)\n", + "print('- MCC: %s' % mlp_test_mcc)\n", + "print('- F1 score: %s' % mlp_test_f1)" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "### Stacking Classifier\n", + "\n", + "Stack of estimators with a final classifier.\n", + "\n", + "Stacked generalization consists in stacking the output of individual estimator and use a classifier to compute the final prediction. Stacking allows to use the strength of each individual estimator by using their output as input of a final estimator.\n", + "\n", + "Note that estimators_ are fitted on the full X while final_estimator_ is trained using cross-validated predictions of the base estimators using cross_val_predict.\n", + "\n", + "class sklearn.ensemble.StackingClassifier(estimators, final_estimator=None, *, cv=None, stack_method='auto', n_jobs=None, passthrough=False, verbose=0)" + ] + }, + { + "cell_type": "code", + "execution_count": 18, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "Model performance for Training set\n", + "- Accuracy: 0.7776785714285714\n", + "- MCC: 0.5613383240343626\n", + "- F1 score: 0.7764597470054544\n", + "----------------------------------\n", + "Model performance for Test set\n", + "- Accuracy: 0.7359285714285714\n", + "- MCC: 0.47849049367542906\n", + "- F1 score: 0.7341756948223885\n" + ] + } + ], + "source": [ + "estimator_list = [\n", + " ('knn',knn),\n", + " ('svm_rbf',svm_rbf),\n", + " ('dt',dt),\n", + " ('rf',rf),\n", + " ('mlp',mlp) ]\n", + "\n", + "# Build stack model\n", + "stack_model = StackingClassifier(\n", + " estimators=estimator_list, final_estimator=LogisticRegression()\n", + ")\n", + "\n", + "# Train stacked model\n", + "stack_model.fit(X_train, y_train)\n", + "\n", + "# Make predictions\n", + "y_train_pred = stack_model.predict(X_train)\n", + "y_test_pred = stack_model.predict(X_test)\n", + "\n", + "# Training set model performance\n", + "stack_model_train_accuracy = accuracy_score(y_train, y_train_pred) # Calculate Accuracy\n", + "stack_model_train_mcc = matthews_corrcoef(y_train, y_train_pred) # Calculate MCC\n", + "stack_model_train_f1 = f1_score(y_train, y_train_pred, average='weighted') # Calculate F1-score\n", + "\n", + "# Test set model performance\n", + "stack_model_test_accuracy = accuracy_score(y_test, y_test_pred) # Calculate Accuracy\n", + "stack_model_test_mcc = matthews_corrcoef(y_test, y_test_pred) # Calculate MCC\n", + "stack_model_test_f1 = f1_score(y_test, y_test_pred, average='weighted') # Calculate F1-score\n", + "\n", + "print('Model performance for Training set')\n", + "print('- Accuracy: %s' % stack_model_train_accuracy)\n", + "print('- MCC: %s' % stack_model_train_mcc)\n", + "print('- F1 score: %s' % stack_model_train_f1)\n", + "print('----------------------------------')\n", + "print('Model performance for Test set')\n", + "print('- Accuracy: %s' % stack_model_test_accuracy)\n", + "print('- MCC: %s' % stack_model_test_mcc)\n", + "print('- F1 score: %s' % stack_model_test_f1)" + ] + }, + { + "cell_type": "code", + "execution_count": 19, + "metadata": {}, + "outputs": [], + "source": [ + "# Run the Line 2 only then model changed!!!\n", + "joblib.dump(stack_model, 'Cardiovascular-prediction-model.joblib')\n", + "\n", + "model = joblib.load('Cardiovascular-prediction-model.joblib') # Model File" + ] + }, + { + "attachments": {}, + "cell_type": "markdown", + "metadata": {}, + "source": [ + "## PREDICTION TESTING" + ] + }, + { + "cell_type": "code", + "execution_count": 21, + "metadata": {}, + "outputs": [ + { + "name": "stdout", + "output_type": "stream", + "text": [ + "######################################################### PROGRAM STARTED #########################################################\n", + "\n", + "Array 0 = [17272 0 110 126 6985 1315 5 2 1 1 1]\n", + "Model predicts STROKE = [1]\n", + "######################################################### PROGRAM FINISHED #########################################################\n" + ] + } + ], + "source": [ + "import random\n", + "\n", + "count=0\n", + "array=[]\n", + "predictionOutcome=[0]\n", + "\n", + "print(\"######################################################### PROGRAM STARTED #########################################################\")\n", + "\n", + "while predictionOutcome == [0]:\n", + " arr1=np.random.randint(23714, size=1)\n", + " arr2=np.random.randint(2, size=1)\n", + " arr3=np.random.randint(251, size=1)\n", + " arr4=np.random.randint(201, size=1)\n", + " arr5=np.random.randint(16021, size=1)\n", + " arr6=np.random.randint(11001, size=1)\n", + " arr7=np.random.randint(6, size=1)\n", + " arr8=np.random.randint(17, size=1)\n", + " arr9=np.random.randint(2, size=1)\n", + " arr10=np.random.randint(2, size=1)\n", + " arr11=np.random.randint(2, size=1)\n", + " \n", + " array = np.concatenate((arr1, arr2, arr3, arr4, arr5, arr6, arr7, arr8, arr9, arr10, arr11), axis=0)\n", + " \n", + " print(\"\\nArray %d =\" %count, array)\n", + " \n", + " predictionOutcome = model.predict([array])\n", + " \n", + " if predictionOutcome == 0:\n", + " print(\"Model predicts NO STROKE = \", predictionOutcome)\n", + " print(\"###################################################################################################################################\")\n", + " else:\n", + " print(\"Model predicts STROKE = \", predictionOutcome)\n", + " \n", + " count+=1\n", + "else:\n", + " print(\"######################################################### PROGRAM FINISHED #########################################################\")" + ] + } + ], + "metadata": { + "kernelspec": { + "display_name": "CardiovascularHeartDisease", + "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": "3.8.13" + }, + "orig_nbformat": 4, + "vscode": { + "interpreter": { + "hash": "289ca0050d77f4c45e5b96d3b6db111060794819dc287c32d07051ebffa70bf3" + } + } + }, + "nbformat": 4, + "nbformat_minor": 2 +} diff --git a/requirements.txt b/requirements.txt new file mode 100644 index 0000000..3673124 --- /dev/null +++ b/requirements.txt @@ -0,0 +1,60 @@ +absl-py +asttokens +backcall +colorama +colorlover +contourpy +cufflinks +cycler +debugpy +decorator +entrypoints +executing +fonttools +importlib-metadata +ipykernel +ipython +ipywidgets +jedi +joblib +jupyter-core +jupyter_client +jupyterlab-widgets +kiwisolver +llvmlite +matplotlib +matplotlib-inline +nest-asyncio +numba +numpy +packaging +pandas +parso +pickleshare +Pillow +plotly +prompt-toolkit +protobuf +psutil +pure-eval +Pygments +pyparsing +python-dateutil +pytz +pywin32 +pyzmq +scikit-learn +scipy +seaborn +six +stack-data +tenacity +tensorboard +threadpoolctl +tornado +traitlets +typing-extensions +wcwidth +widgetsnbextension +wincertstore +zipp diff --git a/splitX.csv b/splitX.csv new file mode 100644 index 0000000..ae65b35 --- /dev/null +++ b/splitX.csv @@ -0,0 +1,70001 @@ +,age,gender,height,weight,ap_hi,ap_lo,cholesterol,gluc,smoke,alco,active +0,18393,2,168,62.0,110,80,1,1,0,0,1 +1,20228,1,156,85.0,140,90,3,1,0,0,1 +2,18857,1,165,64.0,130,70,3,1,0,0,0 +3,17623,2,169,82.0,150,100,1,1,0,0,1 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