K-Nearest Neighbour Model Optimized by Particle Swarm Optimization and Ant Colony Optimization for Heart Disease Classification

Abstract

In this article, we used the Fast Correlation-Based Feature Selection (FCBF) method to filter redundant and irrelevant characteristics in order to improve the quality of heart disease classification. Then, we proposed PA-KNN a classification based K-Nearest Neighbour model optimized by Particle Swarm Optimization (PSO) associated with Ant Colony Optimization (ACO). The proposed mixed approach is applied to the heart disease dataset. The results demonstrate the effectiveness and robustness of the proposed hybrid method in processing various types of data for the classification of heart disease. Therefore, this study examines the different automatic learning algorithms and compares the results using different performance measures, i.e. Accuracy, Precision, Recall, F1-Score, etc. The data set used in this study comes from the UCI’s automatic learning repository, entitled “Heart Disease” Data set. We can be concluded that PA-KNN has demonstrated efficiency and robustness compared to other classification methods.