OPTIMIZING MACHINE LEARNING ALGORITHMS FOR HEART DISEASE PREDICTION

This study addresses the pervasive global issue of heart disease, the leading cause of death in both developed and developing nations. The complex interplay of factors contributing to heart disease involves not only individual well-being but also environmental influences. In an effort to improve predictive models, this research employs state-of-the-art machine learning (ML) techniques on a comprehensive dataset, emphasizing hyperparameter tuning as a crucial aspect for enhancing model accuracy. The research demonstrates the effectiveness of hyperparameter tuning in significantly improving the performance of various ML models. By systematically adjusting hyperparameters, the study showcases a tangible advancement in accuracy. The outcomes of this research hold promise for preventing future occurrences of heart attacks, contributing to proactive measures and interventions. The application of advanced ML techniques and strategic hyperparameter tuning emerges as a powerful approach to refining our understanding of heart disease dynamics. This research not only sheds light on the intricate factors involved but also offers a pathway toward the development of more accurate and reliable predictive models. Ultimately, these findings have the potential to influence public health strategies, fostering a proactive stance in mitigating the impact of heart diseases on global well-being.