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

Haldi, Budiman and Silvia, Ratna and M., Muflih and Usman, Syapotro and Muhammad, Hamdani and M.Rezqy, Noor Ridha (2024) Classification of Heart Disease Using a Stacking Framework of BiGRU, BiLSTM, and XGBoost. Journal of Data Science, 2024 (54). pp. 1-5. ISSN 2805-5160

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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.

Item Type:	Article
Uncontrolled Keywords:	Classification of Heart Disease, Stacking, Bidirectional Gated Recurrent Unit (BiGRU), Bidirectional Long Short-Term Memory (BiLSTM), Extreme Gradient Boosting (XGBoost)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RC Internal medicine > RC0254 Neoplasms. Tumors. Oncology (including Cancer) T Technology > T Technology (General)
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	26 Nov 2024 06:49
Last Modified:	26 Nov 2024 06:49
URI:	http://eprints.intimal.edu.my/id/eprint/2053

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