International Journal of Electrical and Computer Engineering (IJECE) Vol. 14, No. 4, August 2024, pp. 4563~4576 ISSN: 2088-8708, DOI: 10.11591/ijece.v14i4.pp4563-4576  4563 Journal homepage: http://ijece.iaescore.com The use of genetic algorithm and particle swarm optimization on tiered feature selection method in machine learning-based coronary heart disease diagnosis system Wiharto 1 , Yasmin Mufidah 1 , Umi Salamah 1 , Esti Suryani 2 , Sigit Setyawan 3 1 Department of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia 2 Department of Data Science, Faculty of Information Technology and Data Science, Universitas Sebelas Maret, Surakarta, Indonesia 3 Department of Medicine, Faculty of Medicine, Universitas Sebelas Maret, Surakarta, Indonesia Article Info ABSTRACT Article history: Received Oct 26, 2023 Revised Mar 7, 2024 Accepted Mar 16, 2024 Coronary heart disease (CHD) is a leading global cause of death. Early detection is the right step to reduce mortality rates and treatment costs. Early detection can be developed using machine learning by utilizing patient medical record datasets. Unfortunately, this dataset has excessive features which can reduce machine learning performance. For this reason, it is necessary to reduce the number of redundant features and irrelevant data to improve machine learning performance. Therefore, this research proposes a tiered of feature selection model with genetic algorithm (GA) and particle swarm optimization (PSO) to improve the performance of the diagnosis model. The feature selection model is evaluated using parameters derived from the confusion matrix and using the CatBoost machine learning algorithm. Model testing uses z-Alizadeh Sani, Cleveland, Statlog, and Hungarian datasets. The best results for this model were obtained on the z-Alizadeh Sani dataset with 6 selected features from 54 features and the resulting performance for accuracy parameters was 99.32%, specificity 98.57%, sensitivity 100.00%, area under the curve (AUC) 99.28%, and F1-Score 99.37%. The proposed feature selection model is able to provide machine learning performance in the very good category. The diagnostic model proposed is of excellent standard. Keywords: CatBoost algorithm Coronary heart disease Feature selection Genetic algorithm Particle swarm optimization This is an open access article under the CC BY-SA license. Corresponding Author: Wiharto Department of Informatics, Faculty of Information Technology and Data Science, Universitas Sebelas Maret Jl. Ir Sutami No. 36A, Kentingan, Jebres, Surakarta, Indonesia Email: wiharto@staff.uns.ac.id 1. INTRODUCTION Coronary heart disease (CHD) arises from impaired function of the heart and blood vessels. It is a primary cause of mortality worldwide [1]. The World Health Organization (WHO) reported that CHD caused the deaths of up to 17.9 million individuals in 2019. Research by Alizadehsani et al. [2] indicates that 25% of individuals with CHD die unexpectedly and without any preceding symptoms. Early detection of coronary heart disease (CHD) is essential to decrease mortality rates associated with the disease [3]. Currently, CHD diagnosis is developed using machine learning models. However, the process necessitates extensive medical record data. The abundance of medical record data often results in numerous features that do not directly facilitate diagnosis [4] and can ultimately negatively influence machine learning performance. To address these issues, data mining techniques may be employed, specifically through the feature selection method. Feature selection can identify features that enhance the effectiveness of machine learning-based diagnostic models [5].