The objective of this study "Coronary Heart Disease (CHD) Prediction" is to use human-sensor interactions and Machine Learning to derive a model that diagnoses a potential risk of developing CHD in the future.
Coronary artery disease is the narrowing or blockage of the coronary arteries, usually caused by clogging of the arteries due to the buildup of cholesterol and fatty deposits (called plaques) on its inner wall [1]. This restricts the blood flow to the heart and renders it oxygen-starved. This can cause chest pain called Angina. If the blood supply to a portion of the heart muscle is cut off entirely, or if the energy demands of the heart become much greater than its blood supply, a heart attack may occur. According to the World Health Organization, 15 million people suffer from heart attack worldwide each year. Of these, 5 million die and another 5 million are permanently disabled [2].
Statistics show that it is the second leading cause of death for people above the age of 60. Nearly three-quarters of all strokes occur in people over the age of 65. The risk of having a stroke more than doubles each decade after the age of 55 [3].
Early diagnosis of CHD can help in reducing the risk of it developing into a stroke. Thus, in this study, the aim is to predict developing CHD in the next 10 years based on various demographic, medical and behavioural parameters.