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This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

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Mubshr07/heartDiseasePrediction

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DatasetResults_CardiacPrediction (Best Results SMOTE)
DatasetResults_CardiacPrediction (Best Results SMOTE)
 
 
DatasetResults_CardiacPrediction
DatasetResults_CardiacPrediction
 
 
DatasetResults_LSTM_CardiacPrediction
DatasetResults_LSTM_CardiacPrediction
 
 
DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction
DatasetResults_MLP_After_XAI_Features_Selection_Experiment_1_CardiacPrediction
 
 
DatasetResults_MLP_CardiacPrediction
DatasetResults_MLP_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_Extended_2_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_Extended_2_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_Extended_4_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_Extended_4_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_Extended_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_Extended_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_Low_Dimensions _XAI_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_Low_Dimensions _XAI_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_Visualized_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_Visualized_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_XAI_Experiment_3_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_after_XAI_CardiacPrediction 2
DatasetResults_MLP_with_AttentionLayers_after_XAI_CardiacPrediction 2
 
 
DatasetResults_MLP_with_AttentionLayers_imBalancedCardiacPrediction
DatasetResults_MLP_with_AttentionLayers_imBalancedCardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_imBalanced_loaded_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_imBalanced_loaded_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_loaded_Exp3_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_loaded_Exp3_CardiacPrediction
 
 
DatasetResults_MLP_with_AttentionLayers_loaded_SHAP_Features_CardiacPrediction
DatasetResults_MLP_with_AttentionLayers_loaded_SHAP_Features_CardiacPrediction
 
 
DatasetResults_MLP_with_LOW_AttentionLayers_Extended_CardiacPrediction
DatasetResults_MLP_with_LOW_AttentionLayers_Extended_CardiacPrediction
 
 
DatasetResults_PyTorch_CardiacPrediction
DatasetResults_PyTorch_CardiacPrediction
 
 
DatasetResults_RNN_CardiacPrediction
DatasetResults_RNN_CardiacPrediction
 
 
Dataset_Exploration_CardiacPrediction
Dataset_Exploration_CardiacPrediction
 
 
LAB_PC
LAB_PC
 
 
catboost_info
catboost_info
 
 
heartDisease
heartDisease
 
 
.gitignore.txt
.gitignore.txt
 
 
0.1_Correlation_Matrix_DateSetName_CardiacPrediction.png
0.1_Correlation_Matrix_DateSetName_CardiacPrediction.png
 
 
0.Confusion_Matrix_CardiacPrediction.png
0.Confusion_Matrix_CardiacPrediction.png
 
 
0.DataSet_analysis_CardiacPrediction.png
0.DataSet_analysis_CardiacPrediction.png
 
 
1.DataSet_analysis_CardiacPrediction.png
1.DataSet_analysis_CardiacPrediction.png
 
 
CNN_Coronary_Heart_Disease.ipynb
CNN_Coronary_Heart_Disease.ipynb
 
 
CNN_Coronary_Heart_Disease2.ipynb
CNN_Coronary_Heart_Disease2.ipynb
 
 
XAI_Shapely_AUC_TestPoint10
XAI_Shapely_AUC_TestPoint10
 
 
heartDiseaseDL.ipynb
heartDiseaseDL.ipynb
 
 
heartDiseaseDeepNN with Attention.ipynb
heartDiseaseDeepNN with Attention.ipynb
 
 
heartDiseaseDeepNN.ipynb
heartDiseaseDeepNN.ipynb
 
 
heartDiseaseKeras.ipynb
heartDiseaseKeras.ipynb
 
 
heartDiseaseML.ipynb
heartDiseaseML.ipynb
 
 
heartDiseasePyTorch.ipynb
heartDiseasePyTorch.ipynb
 
 
heartDiseaseXAI.ipynb
heartDiseaseXAI.ipynb
 
 
heartDisease_DataSet_Exploration.ipynb
heartDisease_DataSet_Exploration.ipynb
 
 
heartDisease_LSTM.ipynb
heartDisease_LSTM.ipynb
 
 
heartDisease_MLP - Attention - Low Dimensions XAI - Experiment 3.ipynb
heartDisease_MLP - Attention - Low Dimensions XAI - Experiment 3.ipynb
 
 
heartDisease_MLP - Attention - Low Dimensions XAI.ipynb
heartDisease_MLP - Attention - Low Dimensions XAI.ipynb
 
 
heartDisease_MLP - Attention Mechanism - imBalanced.ipynb
heartDisease_MLP - Attention Mechanism - imBalanced.ipynb
 
 
heartDisease_MLP - with Attention Mechanism - Extended.ipynb
heartDisease_MLP - with Attention Mechanism - Extended.ipynb
 
 
heartDisease_MLP - with Attention Mechanism.ipynb
heartDisease_MLP - with Attention Mechanism.ipynb
 
 
heartDisease_MLP - with Low Attention Mechanism - Extended.ipynb
heartDisease_MLP - with Low Attention Mechanism - Extended.ipynb
 
 
heartDisease_MLP - without Attention.ipynb
heartDisease_MLP - without Attention.ipynb
 
 
heartDisease_MLP After XAI Feature Selection - Experiment 1 .ipynb
heartDisease_MLP After XAI Feature Selection - Experiment 1 .ipynb
 
 
heartDisease_MLP.ipynb
heartDisease_MLP.ipynb
 
 
heartDisease_RNN.ipynb
heartDisease_RNN.ipynb
 
 
load Pretrained Model and SHAP_Balanced .ipynb
load Pretrained Model and SHAP_Balanced .ipynb
 
 
load Pretrained Model and SHAP_FeatureSelection.ipynb
load Pretrained Model and SHAP_FeatureSelection.ipynb
 
 
load Pretrained Model and SHAP_ImBalanced.ipynb
load Pretrained Model and SHAP_ImBalanced.ipynb
 
 
model_summary.txt
model_summary.txt
 
 
path_to_save_image.png
path_to_save_image.png
 
 
shap_values.pkl
shap_values.pkl
 
 
testing code.ipynb
testing code.ipynb
 
 

About

This repo is the Machine Learning practice on NHANES dataset of Heart Disease prediction. The ML algorithms like LR, DT, RF, SVM, KNN, NB, MLP, AdaBoost, XGBoost, CatBoost, LightGBM, ExtraTree, etc. The results are good. I also explore the class-balancing (SMOTE) because the original dataset contains only 5% of patient and 95% of healthy record.

Topics

machine-learning random-forest svm xgboost lightgbm logistic-regression adaboost svm-classifier nhanes knn-classification mlp-classifier catboost mlp-networks navies-bayes-classifer dicision-tree extratreesclassifier nhanes-data
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