Cardiovascular_risk_prediction

Cardiovascular disease is a class of diseases that involves the heart or blood vessels. It includes coronary artery disease (CAD), cerebrovascular disease, peripheral artery disease (PAD), and aortic atherosclerosis. Coronary artery disease is the most common type of heart disease affecting millions of people. There are several risk factors associated with it which can be classified into two types, modifiable and non-modifiable factors. As the name suggests, the non-modifiable factors such as age, family history, and gender cannot be altered, while modifiable risk factors such as high blood pressure, high cholesterol, smoking, diabetes, obesity, and lack of physical activity, unhealthy diet, and stress are factors pertaining to lifestyle which can be modified. Hence, accurate and timely detection of such modifiable risks and early intervention is especially relevant in the clinical management of patients with CAD. Machine Learning algorithms can be utilized to identify patterns within patient electronic healthcare records and develop risk prediction models for timely identification of the risk of CAD. In general, the Machine Learning algorithm approach involves ‘training’ an algorithm with a control dataset for which the disease status (disease or no disease) is known, and then applying this trained algorithm to a variable dataset to predict the disease status in patients for whom it is not yet determined.