Predicting and Analyzing Cardiovascular Disease through Ensemble Learning Approaches

Among the toughest assignments for medical professionals is discovering heart illness indicators as quickly as attainable. Coronary artery disease is an urgent issue and should be treated promptly. The diagnosis of heart illness is complicated by a number of factors affecting health, including high pressure, situated cholesterol levels, inconsistent heartbeat, and several more. Therefore, AI can be helpful in recognising and dealing with ailments at an early stage. This research suggests an ensemble-based method to estimate an individual's risk of heart disease using Deep Learning (DL) and Machine Learning (ML) models. In order to forecast cardiovascular disease, we use six classification methods. A large collection of cardiovascular disease cases that is made open to the world is used to train models. To identify key characteristics related to cardiac illness, we employ Random Forest (RF). The research's results show that the ML ensemble model obtains the highest accuracy of 92.75% in predicting diseases. When compared to conventional machine learning methods like K-nearest neighbor (KNN), Random Forest (RF), and Multi-Layer Perceptrons (MLP), the suggested methodology's uniqueness is evaluated by showing a 5.52% increase in efficiency.