Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic Regression, and XGBoost

M. Rezqy, Noor Ridha and Silvia, Ratna and M., Muflih and Haldi, Budiman and Usman, Syapotro and Muhammad, Hamdani (2024) Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic Regression, and XGBoost. Journal of Data Science, 2024 (47). pp. 1-6. ISSN 2805-5160

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Abstract

Diabetes is a chronic condition that requires accurate and timely diagnosis for effective management and treatment. This study introduces an innovative approach to diabetes classification using a stacking framework that combines Bidirectional Long Short-Term Memory (BiLSTM), Logistic Regression, and XGBoost. The study employed an experimental approach by implementing the stacking framework. The two base models used were BiLSTM and Logistic Regression, with BiLSTM achieving an accuracy of 0.9935 and Logistic Regression reaching 0.9869. The stacking framework with XGBoost as the meta-learner achieved a perfect accuracy of 1.0. These findings demonstrate the potential of the stacking framework to improve diabetes classification performance compared to using individual models alone.

Item Type:	Article
Uncontrolled Keywords:	Diabetes Classification, Stacking, Bidirectional Long Short-Term Memory (BiLSTM), Logistic Regression, Extreme Gradient Boosting (XGBoost)
Subjects:	Q Science > QA Mathematics > QA75 Electronic computers. Computer science Q Science > QA Mathematics > QA76 Computer software R Medicine > RA Public aspects of medicine
Depositing User:	Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited:	26 Nov 2024 06:04
Last Modified:	26 Nov 2024 06:04
URI:	http://eprints.intimal.edu.my/id/eprint/2046

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