Empowering decision-making in cardiovascular care: Exploratory data analysis and predictive models for heart attack risk

• Published in: AIP Conference Proceedings
• Main Author: Khan M.R.B.; Islam G.M.N.; Ng P.K.; Zainuddin A.A.; Lean C.P.; Al-Fattah J.; Kamarudin S.I.
• Format: Conference paper
• Language: English
• Published: American Institute of Physics 2024
• Online Access: https://www.scopus.com/inward/record.uri?eid=2-s2.0-85203180013&doi=10.1063%2f5.0224378&partnerID=40&md5=f3133cdf01ea3a6684ef3c1ea7ec4fce

Acute myocardial infarction, commonly referred to as a heart attack, stands as one of the most lethal medical conditions, highlighting the pressing necessity for the effective management of cardiovascular disease. This involves conducting comprehensive data analysis and extracting knowledge essential for diagnosis, regulation, and treatment. Anticipating the occurrence of heart attacks presents a formidable challenge for healthcare professionals, given the intricate nature of the condition that demands both experience and a profound understanding. In the contemporary landscape of medicine, the concealed data landscape conceals invaluable insights that can significantly shape critical decision-making processes. In this research endeavor, a dataset comprising patient records is harnessed to predict an individual's vulnerability to heart attacks. Advanced data visualization techniques are employed to identify pivotal trends and outliers, facilitating the extraction of meaningful and actionable conclusions. This study involves the development of three classifier models for heart attack prediction: Logistic Regression, K Nearest Neighbor, and Support Vector model. © 2024 Author(s).