A Novel Ensemble Approach with HGBDTRF for Enhanced Detection and Prediction of Heart Disease

Abstract

Heart disease is responsible for around one-third of all deaths that occur throughout the globe, as shown by the statistics. The employ of machine learning headed for anticipate cardiac illness have emerged as an important technique for both treating and preventing the ailment as more research is carried out in this area. A unique method that we are working on is called Hybrid Gradient Boosting Decision Tree with Random Forest (HGBDTRF), and it is being developed via the use of ensemble learning in the research paper that we are now working on. Machine learning will be able to make more accurate predictions about heart disease as a result of this. It has been shown by the actual findings that the HGBDTRF algorithm is capable of achieving a prediction accuracy of 95% in the Cleveland cardiac disease dataset, which has 1322 samples.