Tajim Md. Niamat Ullah Akhund1,2,*, Waleed M. Al-Nuwaiser3
CMC-Computers, Materials & Continua, Vol.80, No.3, pp. 3485-3506, 2024, DOI:10.32604/cmc.2024.054222
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 More >
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