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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| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | en en |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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 |
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