Performance analysis and comparison of machine learning algorithms for predicting heart disease

Abstract

Heart disease (HD) is a serious medical condition that has an enormous effect on people's quality of life. Early as well as accurate identification is crucial for preventing and treating HD. Traditional methods of diagnosis may not always be reliable. Non-intrusive methods like machine learning (ML) are proficient in distinguishing between patients with HD and those in good health. The prime objective of this study is to find a robust ML technique that can accurately detect the presence of HD. For this purpose, several ML algorithms were chosen based on the relevant literature studied. For this investigation, two different heart datasets the Cleveland and Statlog datasets were downloaded from Kaggle. The analysis was carried out utilizing the Waikato environment for knowledge analysis (WEKA) 3.9.6 software. To assess how well various algorithms predicted HD, the study employed a variety of performance evaluation metrics and error rates. The findings showed that for both the datasets random forest (RF) is a better option for predicting HD with an accuracy and receiver operating characteristic (ROC) values of 94% and 0.984 for the Cleveland dataset and 90% and 0.975 for the Statlog dataset. This work may aid researchers in creating early HD detection models and assist medical practitioners in identifying HD.