An Ensemble Basedheart Disease Predictionusing Gradient Boosting Decision Tree

Abstract

Heart disease is now one of the deadliest diseases among the world. This crisis is affecting the bulk of people across the globe. Considering the massive death rate and huge amount of people suffering from the disease, the importance of early diagnosis of heart disease has been proven. Known cause for forecasting such a disease exist, but they really do not seem to be adequate. It is critical to develop a standard medical device that can foresee early heart diagnosis and have a more precise diagnosis than existing technologies such as Logistic Regularization, Lasso, Elastic Net, and Lasso Community Regularization.Ensemble classifiers are used in a variety of machine learning models that can increase forecasting ability in healthcare. Four databases are assembled in this paper, and 14 clinical features are fed into Ensemble. Traditional methods like SVM, AdaBoost, Logistic Regularization, and the existing Ensemble Prediction Model are compared to the proposed Ensemble Prediction Model in this paper. Across each experiment, the accuracy rate of the four datasets was 99 percent, outperforming other machine learning techniques and related academic studies. The performance metrics clearly show that the developed ensemble learning approach is superior. The findings show that the suggested ensemble can accurately predict the risk of cardiovascular disease.