Using Ensemble Approaches and Different Sampling Techniques to Handle Class Imbalance Challenges in Coronary Heart Disease Prediction

Coronary Heart Disease (CHD) has a significant impact on death rates worldwide; therefore, early and accurate prediction is vital for enhancing timely prevention and improving patient outcomes. However, predicting CHD is challenging because of the imbalanced nature of the dataset. A number of sampling techniques are proposed to overcome this challenge, including random oversampling, undersampling, and synthetic minority oversampling techniques (SMOTE), as well as three ensemble methods, including XGBoost, Random Forest, and AdaBoost. Incorporating these techniques minimizes classifier bias in favor of the majority group and emphasizes the learning of minority concepts appropriately. Undersampling techniques give the highest accuracy in AdaBoost, which is 53%. However, the oversampling approach and SMOTE achieved the highest accuracy of 82%, which is in the Random Forest algorithm. The sensitivity and precision scores are also increased in the proposed model. Therefore, our proposed approach offers a trustworthy and effective solution to the class imbalance issue in CHD prediction, and it has the potential to be used in other healthcare areas that face similar challenges.