Authors:
Malini Premakumari William, S. Briskline Kiruba, M. Sakthivanitha, Edwin Shalom Soji, G. Arun
Addresses:
Department of Computer Science, Bishop Heber College, Trichy, Tamil Nadu, India. Department of Information Technology, Vels Institute of Science Technology and Advance Studies, Chennai, Tamil Nadu, India. Department of Computer Science, Bharath Institute of Higher Education and Research, Chennai, Tamil Nadu, India. Department of Electrical and Electronics Engineering, Dhaanish Ahmed College of Engineering, Chennai, Tamil Nadu, India.
Abstract:
This research bases the knowledge of the onset of CVD and applies advanced methodologies for machine learning on a number of cardiovascular health indicators. The dataset of interest contains all the factors, including age, cholesterol levels, resting blood pressure, achieved maximum heart rate, ST depression, and the number of major vessels. Good preprocessing of data and extensive model training in this study will unravel the complex patterns and relationships it contains, including the principal contributors to CVD, namely, family history, cholesterol levels, and age. Such findings are of critical importance for targeted preventive strategies, while health professionals are given powerful information on how to use data to personalize interventions according to risk profiles. Finally, such a study thus reveals the fantastic transformations data-powered approaches could have on healthcare decision-making, pushes forward the frontiers of precision medicine, and eventually contributes to better cardiovascular health outcomes worldwide.
Keywords: Cardiovascular Diseases (CVDs); Sophisticated Machine Learning; Rigorous Model Training; Relationships Embedded; Primary Determinants; Targeted Preventive Strategies; Data-Driven Insights; Decision-Making.
Received: 10/02/2024, Revised: 03/04/2024, Accepted: 01/06/2024, Published: 05/09/2024
AVE Trends in Intelligent Health Letters, 2024 Vol. 1 No. 3 , Pages: 137-157