Particle Swarm Optimisation-Support Vector Machine Optimised by Association Rules for Detecting Factors Inducing Heart Diseases

Abstract

AbstractExisting systems for diagnosing heart diseases are time consuming, expensive, and error prone. Aiming at this, a detection algorithm for factors inducing heart diseases based on a particle swarm optimisation-support vector machine (PSO-SVM) optimised by association rules (ARs) was proposed. Firstly, AR was used to select features from a disease data set so as to train feature sets. Then, PSO-SVM was used to classify training and testing sets, and then the factors inducing heart diseases were analysed. Finally, the effectiveness and reliability of the proposed algorithm was verified by experiments on the UCI Cleveland data set with confidence as the index. The experimental results showed that females have less risk of having a heart attack than males. Irrespective of gender, once diagnosed with chest pain without symptoms and angina caused by exercise, people are more likely to suffer from heart disease. Moreover, compared with another two advanced classification algorithms, the proposed algorithm showed better classification performance and therefore can be used as a powerful tool to help doctors diagnose and treat heart diseases.