[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121215-en":3,"doc-seo-121215-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},121215,7971461740909,"Levi","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets","Diabetes prediction from clinical datasets is essential for timely intervention, yet class imbalance where non-diabetic cases dominate can severely degrade machine learning performance. The study proposes a robust predictive framework that combines advanced imbalance-handling strategies with feature engineering and resampling to improve predictive accuracy. Evaluation on three datasets—PIMA, Diabetes Dataset 2019, and BIT_2019—demonstrates adaptability across different data environments. Results emphasize the importance of model selection and imbalance mitigation for reliable, generalizable diabetes classification.","TYPE Original Research PUBLISHED 07 January 2025 DOI 10. 3389/frai.2024.1499530  \nOPEN ACCESS  \nEDITED BY  \nPrasanna Santhanam,  \nJohns Hopkins University, United States  \nREVIEWED BY  \nJayakumar Kaliappan,  \nVellore Institute of Technology (VIT), India Antonio Sarasa-Cabezuelo, Complutense University of Madrid, Spain  \n*CORRESPONDENCE  \nHaitham F. Abdallah  \n [haitham.freag@yahoo.com](haitham.freag@yahoo.com)  \nRECEIVED 21 September 2024  \nACCEPTED 12 December 2024  \nPUBLISHED 07 January 2025  \nCITATION  \nAbousaber I, Abdallah HF and El-Ghaish H (2025) Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets. Front. Artif. Intell. 7:1499530 .  \ndoi: 10.3389/frai.2024.1499530  \nCOPYRIGHT  \n© 2025 Abousaber, Abdallah and El-Ghaish. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nRobust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets  \nInam Abousaber1 , Haitham F. Abdallah2* and Hany El-Ghaish3  \n1 Department of Information Technology, Faculty of Computers and Information Technology, University of Tabuk, Tabuk, Saudi Arabia, 2 Department of Electronics and Electrical Communication, Higher Institute of Engineering and Technology, Kafr El Sheikh, Egypt, 3 Department of Computer and Automatic Control, Faculty of Engineering, Tanta University, Tanta, Egypt  \nIntroduction: Diabetes prediction using clinical datasets is crucial for medical data analysis. However, class imbalances, where non-diabetic cases dominate, can significantly a􀀀ect machine learning model performance, leading to biased predictions and reduced generalization.  \nMethods: A 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.  \nResults: Rigorous 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.  \nDiscussion: The experimental results highlight the critical role of model selection and imbalance mitigation in achieving reliable and generalizable diabetes predictions. This study o􀀀ers significant contributions to medical informatics by proposing a robust data-driven framework that addresses class imbalance challenges, thereby advancing diabetes prediction accuracy.  \nKEYWORDS  \ndiabetes detection, imbalance handling methods, imbalanced datasets, machine learning, statistical analysis  \n1 Introduction  \nDiabetes is a chronic disease that has reached epidemic proportions globally, a􀀓ecting ∼537 million adults as of 2021, with projections indicating a rise to 783 million by 2045  \n(Saeedi et al., 2019) . Characterized by the body’s inability to produce or e􀀓ectively use insulin, diabetes leads to elevated blood glucose levels, which, if not managed, can result in severe complications such as cardiovascular disease, kidney failure, blindness, and lower limb amputations (Demir et al., 2021) . These complications diminish the quality of life for millions of people and signi􀀂cantly increase healthcare costs, placing a considerable burden on healthcare systems worldwide (Tomic et al., 2022) .  \nEarly detection of diabetes is critical for timely intervention, which can signi􀀂cantly reduce the risk of these complications and improve patient outcomes (Jones et al., 2021) . By diagnosing diabetes early, patients can receive appropriate treatment, make necessary  \nFro","cbCaiq93ImMYt7n7","https://ap.wps.com/l/cbCaiq93ImMYt7n7","pdf",4561015,1,25,"English","en",105,"# Introduction\n## Diabetes and the need for early detection\n## Limitations of traditional diagnostic methods\n## Promise and challenges of machine learning\n# Methods\n## Predictive framework and imbalance handling\n# Results\n## Experiments on PIMA and other datasets\n# Discussion\n## Model selection and generalizable prediction","[{\"question\":\"Why does class imbalance affect diabetes classification models?\",\"answer\":\"Non-diabetic cases typically outnumber diabetic cases in medical datasets, which can bias models toward the majority class and reduce minority-class detection performance.\"},{\"question\":\"What does the proposed predictive framework integrate to improve accuracy?\",\"answer\":\"It combines feature engineering with advanced imbalance-handling techniques, including resampling strategies, alongside optimized machine learning algorithms.\"},{\"question\":\"Which datasets are used to evaluate the framework?\",\"answer\":\"The framework is tested on three datasets: PIMA, Diabetes Dataset 2019, and BIT_2019.\"}]","Robust predictive framework for diabetes classification using optimized machine learning on imbalanced datasets | 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