Improving Performance of KNN and C4.5 using Particle Swarm Optimization in Classification of Heart Diseases

Abstract

Heart disease is a major problem that must be overcome for human life. In recent years, the volume of medical data related to heart disease has increased rapidly, and various heart disease data has collaborated with information technology such as machine learning to detect, predict, and classify diseases. This research aims to improve the performance of machine learning classification methods, namely K-Nearest Neighbor (KNN) and Decision Tree (C4.5) with particle swarm optimization (PSO) feature in cases of heart disease. In this research, a comparison was made of the performance of the PSO-based K-NN and C4.5 algorithms. Following experiments employing PSO optimization to improve the K-NN and C4.5 algorithms, the findings indicated that the K-NN algorithm performed exceptionally well with PSO, achieving an accuracy of 89.09%, precision of 89.61%, recall of 90.79%, and an AUC value of 0.935.