Abstract

Heart disease remains one of the leading causes of mortality worldwide, highlighting the urgent need for accurate and efficient prediction systems. In this study, a hybrid framework combining machine learning (ML) classifiers with particle swarm optimization (PSO) was proposed for heart disease classification. Two hybrid models, random forest (RF)–PSO and decision tree (DT)–PSO, were developed using the Cleveland heart disease dataset. Data preprocessing included normalization, encoding, and feature selection to enhance model efficiency. PSO was employed to optimize hyperparameters of RF and DT classifiers. The results demonstrated that RF–PSO achieved superior performance, with an accuracy of 94%, compared to DT–PSO, which attained 89% accuracy. The findings suggest that RF–PSO provides a robust and reliable framework for early detection of heart disease, supporting healthcare professionals in timely diagnosis and intervention.

Keywords

Heart disease prediction, Machine learning, Random forest (RF), Decision tree (DT), Particle swarm optimization (PSO), Cleveland heart disease dataset.

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