Effective Heart Disease Prediction Using Hybrid Machine Learning Techniques

TLDR

Heart disease remains a leading global cause of death, and accurate cardiovascular disease prediction is a critical yet challenging task; machine learning has shown promise in clinical data analysis and IoT, though existing studies offer only limited insight into heart disease prediction. The study introduces a novel feature‑selection approach that leverages machine learning to enhance cardiovascular disease prediction accuracy. The prediction framework combines multiple feature sets with various classification algorithms, notably a hybrid random forest with a linear model (HRFLM). Using this hybrid model, the authors achieved an 88.7 % accuracy in predicting heart disease.

Abstract

Heart disease is one of the most significant causes of mortality in the world today. Prediction of cardiovascular disease is a critical challenge in the area of clinical data analysis. Machine learning (ML) has been shown to be effective in assisting in making decisions and predictions from the large quantity of data produced by the healthcare industry. We have also seen ML techniques being used in recent developments in different areas of the Internet of Things (IoT). Various studies give only a glimpse into predicting heart disease with ML techniques. In this paper, we propose a novel method that aims at finding significant features by applying machine learning techniques resulting in improving the accuracy in the prediction of cardiovascular disease. The prediction model is introduced with different combinations of features and several known classification techniques. We produce an enhanced performance level with an accuracy level of 88.7% through the prediction model for heart disease with the hybrid random forest with a linear model (HRFLM).