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: M. Rezqy, Noor Ridha, Silvia, Ratna, M., Muflih, Haldi, Budiman, Usman, Syapotro, Muhammad, Hamdani
Format: Article
Language:English
English
Published: INTI International University 2024
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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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spelling my-inti-eprints.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
institution INTI International University
building INTI Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider INTI International University
content_source INTI Institutional Repository
url_provider http://eprints.intimal.edu.my
language English
English
topic QA75 Electronic computers. Computer science
QA76 Computer software
RA Public aspects of medicine
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
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.
format Article
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
title 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_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_sort diabetes classification using a framework stacking of bilstm, logistic regression, and xgboost
publisher INTI International University
publishDate 2024
url 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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score 13.222552