Copyright © 2024 The Authors. Published by Tech Science Press. This work is licensed under a Creative Commons Attribution 4.0 International License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. ech T Press Science DOI: 10.32604/cmc.2024.054222 ARTICLE Improving Prediction Efficiency of Machine Learning Models for Cardiovascular Disease in IoST-Based Systems through Hyperparameter Optimization Tajim Md. Niamat Ullah Akhund 1 , 2 , * and Waleed M. Al-Nuwaiser 3 1 Department of Computer Science and Engineering (CSE), Daffodil International University, Dhaka, 1216, Bangladesh 2 Graduate School of Science and Engineering, Saga University, Saga, 8408502, Japan 3 Computer Science Department, College of Computer and Information Sciences, Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, 11623, Saudi Arabia *Corresponding Author: Tajim Md. Niamat Ullah Akhund. Email: tajim.cse@diu.edu.bd Received: 22 May 2024 Accepted: 31 July 2024 Published: 12 September 2024 ABSTRACT This study explores the impact of hyperparameter optimization on machine learning models for predicting cardiovascular disease using data from an IoST (Internet of Sensing Things) device. Ten distinct machine learning approaches were implemented and systematically evaluated before and after hyperparameter tuning. Significant improvements were observed across various models, with SVM and Neural Networks consistently showing enhanced performance metrics such as F1-Score, recall, and precision. The study underscores the critical role of tailored hyperparameter tuning in optimizing these models, revealing diverse outcomes among algorithms. Decision Trees and Random Forests exhibited stable performance throughout the evaluation. While enhancing accuracy, hyperparameter optimization also led to increased execution time. Visual representations and compre- hensive results support the findings, confirming the hypothesis that optimizing parameters can effectively enhance predictive capabilities in cardiovascular disease. This research contributes to advancing the understanding and application of machine learning in healthcare, particularly in improving predictive accuracy for cardiovascular disease management and intervention strategies. KEYWORDS Internet of sensing things (IoST); machine learning; hyperparameter optimization; cardiovascular disease prediction; execution time analysis; performance analysis; wilcoxon signed-rank test 1 Introduction In the current era of Artificial Intelligence (AI), Machine Learning (ML), and Robotics, cardiovas- cular disease remains a significant cause of mortality globally, necessitating effective predictive models for early diagnosis and intervention. Researchers have increasingly turned to machine learning algo- rithms to predict heart attacks, leveraging diverse datasets to improve accuracy. This study focuses on the pivotal role of hyperparameter optimization in refining machine learning models for cardiovascular disease prediction. By implementing ten distinct ML approaches and systematically evaluating their