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

• 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/2046/1/jods2024_47.pdf; http://eprints.intimal.edu.my/2046/2/587; http://eprints.intimal.edu.my/2046/; http://ipublishing.intimal.edu.my/jods.html

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,...