Novel Feature Engineering for Heart Disease Risk Prediction Using Optimized Machine Learning

Abstract

Heart disease is the leading cause of death and killing millions of people every year around the world. Various automated intelligent systems to predict the heart disease risk have been developed by current research works. However, these studies have drawbacks such as the inability to pick important features, lack of hyperparameter optimization, and varied performance from one model to another. In this work, proposed an unconventional feature engineering in which a Principal Component Analysis was performed on heart dataset to extract the transformed features and selected significant ones among them using Relief method. The hyperparameters of Support Vector Machine, K-Nearest Neighbors, J4S Decision Tree, AdaBoost Ml, Bagging, and Rotation Forest classifiers were optimized and performed machine learning classification using 10-fold cross-validation. The proposed work produced highest accuracy of 98.43% and AUC of 0.996 using KNN, while the Rotation Forest reached the accuracy of 98.25% and best AUC of 0.997.