AN EVALUATION OF HEART DISEASE PREDICTION USING AN EXTREME GRADIENT BOOSTING ALGORITHM
Daftar Isi:
- Heart disease has recorded the most death cause in the world. A lot of researchers are trying to find better and more reliable machine learning to diagnose heart disease. Accuracy and the speed of computation become the main concern when classifying heart disease at its early stages related to human life. This paper researched about Extreme Gradient Boosting (XGBoost) as an ensemble learning with boosting method to predict heart disease. The data will be preprocessed using handling missing value and removing outliers. The algorithm will be compared with 2 different datasets (with feature selection and without feature selection). The outcome of this research hopefully can present the performance result of the Extreme Gradient Boosting algorithm using tenfold cross-validation and performance measures (Precision, Recall, F1-score, ROC Area, and Accuracy) when using feature selection and without using feature selection.