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,...
| Main Authors: | , , , , , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| 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 |
| Summary: | 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. |
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