Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets
IntroductionDiabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive...
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| Format: | Article |
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Frontiers Media S.A.
2025-01-01
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| Series: | Frontiers in Artificial Intelligence |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/frai.2024.1499530/full |
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| author | Inam Abousaber Haitham F. Abdallah Hany El-Ghaish |
| author_facet | Inam Abousaber Haitham F. Abdallah Hany El-Ghaish |
| author_sort | Inam Abousaber |
| collection | DOAJ |
| description | IntroductionDiabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.ResultsRigorous testing was conducted on three datasets—PIMA, Diabetes Dataset 2019, and BIT_2019—demonstrating the robustness and adaptability of the methodology across varying data environments.DiscussionThe experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy. |
| format | Article |
| id | doaj-art-e63692ce8ecd469e9a8b4703fe27a80c |
| institution | Kabale University |
| issn | 2624-8212 |
| language | English |
| publishDate | 2025-01-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Artificial Intelligence |
| spelling | doaj-art-e63692ce8ecd469e9a8b4703fe27a80c2025-01-07T06:42:25ZengFrontiers Media S.A.Frontiers in Artificial Intelligence2624-82122025-01-01710.3389/frai.2024.14995301499530Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasetsInam Abousaber0Haitham F. Abdallah1Hany El-Ghaish2Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi ArabiaDepartment of Electronics and Electrical Communication, Higher Institute of Engineering and Technology, Kafr El Sheikh, EgyptDepartment of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, EgyptIntroductionDiabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly affect machine learning model performance, leading to biased predictions and reduced generalization.MethodsA novel predictive framework employing cutting-edge machine learning algorithms and advanced imbalance handling techniques was developed. The framework integrates feature engineering and resampling strategies to enhance predictive accuracy.ResultsRigorous testing was conducted on three datasets—PIMA, Diabetes Dataset 2019, and BIT_2019—demonstrating the robustness and adaptability of the methodology across varying data environments.DiscussionThe experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study offers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.https://www.frontiersin.org/articles/10.3389/frai.2024.1499530/fulldiabetes detectionimbalance handling methodsimbalanced datasetsmachine learningstatistical analysis |
| spellingShingle | Inam Abousaber Haitham F. Abdallah Hany El-Ghaish Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets Frontiers in Artificial Intelligence diabetes detection imbalance handling methods imbalanced datasets machine learning statistical analysis |
| title | Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| title_full | Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| title_fullStr | Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| title_full_unstemmed | Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| title_short | Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| title_sort | robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets |
| topic | diabetes detection imbalance handling methods imbalanced datasets machine learning statistical analysis |
| url | https://www.frontiersin.org/articles/10.3389/frai.2024.1499530/full |
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