Heart Disease Detection System Using Gradient Boosting Technique

Cardiac disease is defined as abnormal heart function caused by a variety of factors. Heart Failure (HF), Coronary Artery Disease (CAD), and Cardiovascular Disease (CV) are the three most frequent forms of heart disease. Coronary artery blockage or narrowing is the leading cause of heart failure. Many researchers have created various methods for the automated diagnosis of heart failure. The recently suggested techniques increases the accuracy of heart failure diagnosis on both testing and training the model. In this proposed system, supervised learning i.e., gradient boosting technique is used to detect the heart failure. The proposed diagnostic system uses gradient boosting algorithm (GB) for training & testing the model. Gradient boosting classifier is used to extract the features of heart diagnosis. In this experiment, the detection of heart failure disease by using Cleveland Dataset. The proposed system, achieves an accuracy of 97.10% which compares with an other methods.