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

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

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,...

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Bibliographic Details
Main Authors:	M. Rezqy, Noor Ridha, Silvia, Ratna, M., Muflih, Haldi, Budiman, Usman, Syapotro, Muhammad, Hamdani
Format:	Article
Language:	English English
Published:	INTI International University 2024
Subjects:	QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine
Online Access:	http://eprints.intimal.edu.my/2046/ http://eprints.intimal.edu.my/2046/1/jods2024_47.pdf http://eprints.intimal.edu.my/2046/2/587

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