Intelligent Prediction of Cardiovascular Disease Mortality Using Machine Learning Techniques

Abstract

This study focuses on predicting cardiovascular disease (CVD) mortality using various machine learning (ML) techniques. A diverse set of parameters from different categories within the Sleep Heart Health Study (SHHS) dataset is leveraged, and ML techniques including LR, KNN, SVM, RF, ETC, and SGD, are employed. To ensure the reliability of these techniques, 10-fold cross-validation is applied. Furthermore, the mutual information technique with K-fold stratified cross-validation is used to determine feature importance, enhancing the model’s interpretability. The proposed approach predicts CVD mortality over a 10 to 15-year period and aims to identify influential parameters to facilitate timely interventions and lifestyle improvements for patients, ultimately contributing to an increased lifespan. Among the algorithms, KNN outperforms others, achieving an accuracy of 77%, an F1-score of 77%, an AUC of 79%, a sensitivity of 77.34%, and a specificity of 76.56%.