Improving Classification and Regression Tree Performance Using Bagging in Heart Disease Diagnosis

Abstract

Heart disease is one of the leading causes of death worldwide, necessitating fast and accurate diagnostic methods for effective prevention. One approach that can be used is data mining, particularly classification methods to analyze health data. The Classification and Regression Tree (CART) algorithm is known for its interpretability but has a drawback in terms of model stability against data variation. To address this issue, the Bootstrap Aggregating (Bagging) technique is applied to improve the model’s stability and accuracy. This study aims to implement and evaluate the effectiveness of the Bagging technique in enhancing the performance of the CART algorithm for heart disease diagnosis. The data used in this study consists of three datasets available on the Kaggle platform: Heart Disease, Heart Disease Cleveland, and Heart Disease Prediction. The model is built under two conditions: using default parameters and using parameters optimized through the Grid Search method. The research process includes data preprocessing (data type adjustment, handling missing values, and outlier detection), training of two types of classification models (single CART and CART with Bagging), and evaluation based on accuracy metrics. The results show that the application of the Bagging technique consistently improves the accuracy of the CART algorithm. Under default parameters, accuracy increased from 72.89% to 78% (Heart Disease), 81.89% to 85.78% (Heart Disease Cleveland), and 77.44% to 82.44% (Heart Disease Prediction). With tuned parameters, accuracy increased from 75% to 84% (Heart Disease), 77% to 83% (Heart Disease Cleveland), and remained at 83% (Heart Disease Prediction). Therefore, the Bagging technique is proven effective in enhancing the accuracy and stability of the CART model for heart disease diagnosis.