Early Cardiovascular Disease Detection Using Ensemble Methods

Finding cardiovascular disease symptoms as early as feasible is one of the most difficult tasks for medics. The annual death toll from cardiovascular disease is high around the world. Heart disease must be promptly treated because it is a serious problem. It is difficult to diagnose cardiac disease because of numerous factors that might impair health, including high blood pressure, raised cholesterol, an irregular heartbeat, and many more. Thus, early disease detection and treatment are made possible by artificial intelligence. This study suggests an ensemble-based method for predicting a person's risk of cardiovascular disease using machine learning (ML). In order to forecast cardiovascular disease, we use five categorization methods and by tuning the hyperparameters of the models, we get an accuracy of 92.96%. Majority voting was employed to reduce the number of false results.