Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost

Abstract

This study aims to develop a heart disease classification model using an ensemble approach by leveraging a Stacking framework that combines BiGRU, BiLSTM, and XGBoost models. In this research, the BiGRU and BiLSTM models are utilized as base models to extract temporal and spatial features from sequential data, while XGBoost is employed as a metamodel to perform the final classification based on the features generated by the two base models. The test results show that the BiGRU model achieves an accuracy of 0.77, while the BiLSTM model achieves an accuracy of 0.85. By applying the Stacking technique using XGBoost as the meta-model, the classification accuracy significantly increases to 0.92. These findings indicate that the Stacking framework can effectively enhance heart disease classification performance, making it a potentially powerful tool for medical applications in heart disease diagnosis.