Early Prediction of Cardiovascular Disease Using Machine Learning Algorithms

Cardiovascular disease (CVD) is regarded as one of the world's leading causes of death. Individuals who are dealing with various risk factors such as high blood cholesterol, obesity (overweight), hypertension, and diabetes are more susceptible to CVD and thus need early detection. Advancements of technologies are assembling terabytes of data every day from the healthcare industry to keep records. However, this data is not mined well to anticipate the likelihood of a patient getting a cardiopulmonary arrest. Therefore, with the assistance of disparate machine learning and data mining techniques, it is feasible to extract useful insights and discover hidden patterns from the datasets to get a more accurate diagnosis and decision-making. The paper aims to review different research papers with comparative results that have been done on the prognostication of CVD to get an integrated, synthesized overview of machine learning techniques, their performance measures in several datasets and to also make vital conclusions. From the study, we observed that various techniques such as decision trees and artificial neural network (ANN) give the highest CVD prediction system accuracy in different scenarios. This procedure could possibly be useful for cardiologists to forecast the occurrence of cardiovascular disease beforehand and come up with proper medical treatment.