An Improved Auto Categorical PSO with ML for Heart Disease Prediction

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