abstract

Numerous serious illnesses can be cured by advancements in the pharmaceutical industry and the healthcare system. However, the main obstacle to preventing fatalities is determining the right timing for detection and the precision of the detection technique. A major illness that can be fatal to a person is coronary disease, which is characterized by a heart condition that tends to malfunction. Men are more likely than women to get chronic diseases, especially heart disease. Heart disease can be broadly classified as coronary artery disease, arrhythmias, and congenital heart illnesses, though it is primarily caused by a variety of factors. Heart disease has no symptoms at first, and people are more vulnerable if a chronic heart disease event takes place. According to Yang and Garibaldi, age, gender, high blood pressure, cholesterol, smoking, diabetes, physical inactivity, and obesity are common risk factors for heart disease. These risk variables can be divided into categories that are manageable and unmanageable. According to Hajar et al. (2017), non-manageable risk variables include age and family history, while manageable risk factors include smoking, physical activity, diet, and obesity. The suggested framework presents a novel method based on the blockages of the heart's major blood channels for determining the severity of heart illnesses utilizing a multilayer perceptron approach.