Comparison PSO And IWPSO Performance In Optimizing Decision Tree Algorithm On Heart Disease Dataset

Abstract

Heart disease, one of the most common and potentially fatal chronic diseases, has become a major focus in global health efforts. In this study, researchers used the decision tree algorithm on the heart disease dataset with the stages of the decision algorithm including the EDA, Split Data, and Decision tree modeling stages. Furthermore, hyperparameters use PSO and IWPSO to optimize the algorithm. The purpose of this research is to analyze the performance of Particle Swarm Optimization (PSO) and Inertia Weight Particle Swarm Optimization (IWPSO) in heart disease prediction based on relevant datasets. PSO and IWPSO were applied to the heart disease dataset, with the results showing an accuracy rate of 78% for PSO and 84% for IWPSO. These results indicate that IWPSO provides significant performance improvement compared to PSO in the context of heart disease prediction. The implications of these findings can support the development of more efficient prediction systems for early detection of heart disease, making a positive contribution to prevention efforts and further treatment of this critical health condition. In addition, the purpose of this research is to continue research in the form of C4.5 on heart disease with a result of 80.43%. In this study, IWPSO got the best accuracy of 84.23% greater than previous research. The results of this study are to provide insight that PSO and IWPSO hyperparameters can optimize decision trees in handling heart disease datasets and continue research.