MACHINE LEARNING TECHNIQUES TO DETECT HEART DISEASE: A REVIEW
Hasniza Tawyer, Hanirah Mohamad Nur & Suriati Binti Mustaffa
Kolej Komuniti Kota Tinggi
Ischemic heart disease is one of the most widely spread cardiovascular diseases and the leading cause of mortality in Malaysia. Myocardial infarction, also known as a heart attack, is caused by artery blockage complications. Automating heart disease screening can lead to successful treatments for preventing heart failure, especially at the early stages, as it signifies the disease has reached a threatening phase. The objective of this paper is to review the state-of-the-art machine-learning techniques used for heart disease detection. This paper also presented a comprehensive comparison study between machine learning techniques through their performance metrics, which have been presented in the context of a summary. This paper also addresses the challenges and prospective developments in the field of predictive modeling for heart disease detection.
Heart disease, classification, machine learning, algorithms