Predicting Heart Disease Using Machine Learning Techniques on Electronic Health Records Data

Abstract

heart disease is a prevalent cause of mortality worldwide, and the ability to identify and prevent this ailment at an early stage is crucial for enhancing patient outcomes. Recently, researchers have focused on utilizing Machine-Learning (ML) techniques to predict risk factors and the occurrence of heart disease. This study aims to identify the optimal parameters for accurately predicting the occurrence of heart disease through the fine-tuning of ML models' parameters. This study incorporated a blend of datasets from Cleveland, Hungary, Switzerland, and Long Beach V, which consisted of patients' medical histories and laboratory test outcomes. The collected data were employed to predict the occurrence of heart disease through diverse ML algorithms, such as KNN, decision tree, and SVM. Bayesian optimization was also utilized to fine-tune the hyperparameters of the ML algorithms for heart disease prediction. The findings demonstrated that the ML models achieved high accuracy in predicting the occurrence of heart disease, with the optimized KNN model achieving the highest accuracy rate of 100%.