Heart disease approach using modified random forest and particle swarm optimization

Abstract

For the past two decades, heart disease has been classified as one of the main causes of mortality globally. Fortunately, most researchers focused on data mining techniques, which play an important role in accurately predicting heart disease to develop their models. In this paper, by combining particle swarm optimization (PSO) and modified random forest (MRF), a new approach (PSO-MRF) is proposed to predict heart disease. The main purpose is to select the important features after the bootstrap method for each decision tree in the random forest, and then optimize the MRF by the PSO algorithm. The experiments are carried out using the publicly accessible UCI heart disease datasets. Thorough experimental analysis demonstrates that our approach has outperformed the random forest algorithm as well as many other classifiers. This model helps doctors and researchers improve the diagnosis and treatment of heart disease, resulting in more prompt, accurate patient care.