Boosting Algorithm for Classifying Heart Disease Diagnose

Abstract

The heart is a component of the human body that is responsible for pumping blood and distributing oxygen throughout the body. Hospitals and doctors are still checking heart disease diagnoses manually at this time. However, this method is expensive and time-consuming. In this study, the Gradient Tree Boosting (GTB) algorithm was used to detect patients diagnosed with heart disease (disease and no disease). The purpose of the method is to provide convenience to obtain early information on heart health. With the dataset provided from the UCI Machine Learning Repository, there are 13 supporting features to detect heart disease with a total of 304 data. This study uses the GTB model with the best four parameters and utilizes feature selection which is used to classify. From the results of the study to get a recall score of 0.98, the proposed method succeeded in classifying patients who were diagnosed with heart disease correctly.