An Improved Auto Categorical PSO with ML for Heart Disease Prediction

Abstract

Cardiovascular or heart diseases consist a global major health concern. Cardiovascular diseases have the highest mortality rate worldwide, and the death rate increases with age, but an accurate prognosis at an early stage may increase the chances of surviving. In this paper, a combined approach, based on Machine Learning (ML) with an optimization method for the prediction of heart diseases is proposed. For this, the Improved Auto Categorical Particle Swarm Optimization (IACPSO) method was utilized to pick an optimum set of features, while ML methods were used for data categorization. Three heart disease datasets were taken from the UCI ML library for testing: Cleveland, Statlog, and Hungarian. The proposed model was assessed for different performance parameters. The results indicated that, with 98% accuracy, Logistic Regression (LR) and Support Vector Machine by Grid Search (SVMGS) performed better for the Statlog, SVMGS outperformed on the Cleveland, while the LR, Random Forest (RF), Support Vector Machine (SVM), and SVMGS performed better with 97% accuracy on the Hungarian dataset. The outcomes were improved by 3 to 33% in terms of performance parameters when ML was applied with IACPSO.