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 |
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| author | M. Rezqy, Noor Ridha Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Muhammad, Hamdani |
| author_facet | M. Rezqy, Noor Ridha Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Muhammad, Hamdani |
| author_sort | M. Rezqy, Noor Ridha |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:34Z |
| format | Article |
| id | intimal-2046 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:34Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20462024-11-26T06:04:22Z http://eprints.intimal.edu.my/2046/ Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic Regression, and XGBoost M. Rezqy, Noor Ridha Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Muhammad, Hamdani QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine 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. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2046/1/jods2024_47.pdf text en cc_by_4 http://eprints.intimal.edu.my/2046/2/587 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 http://ipublishing.intimal.edu.my/jods.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine M. Rezqy, Noor Ridha Silvia, Ratna M., Muflih Haldi, Budiman Usman, Syapotro Muhammad, Hamdani Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic Regression, and XGBoost |
| title | Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic
Regression, and XGBoost |
| title_full | Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic
Regression, and XGBoost |
| title_fullStr | Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic
Regression, and XGBoost |
| title_full_unstemmed | Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic
Regression, and XGBoost |
| title_short | Diabetes Classification Using a Framework Stacking of BiLSTM, Logistic
Regression, and XGBoost |
| title_sort | diabetes classification using a framework stacking of bilstm, logistic
regression, and xgboost |
| topic | QA75 Electronic computers. Computer science QA76 Computer software RA Public aspects of medicine |
| url | http://eprints.intimal.edu.my/2046/ http://eprints.intimal.edu.my/2046/ http://eprints.intimal.edu.my/2046/1/jods2024_47.pdf http://eprints.intimal.edu.my/2046/2/587 |