[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121060-en":3,"doc-seo-121060-105":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},121060,2336464648322,"Aria","https://ap-avatar.wpscdn.com/avatar/2200025388227c56fec?_k=1778556882303663488",7,"Healthcare","Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction","Diabetes remains a pervasive global health challenge, with diagnostic processes complicated by its subtle onset and broad long-term consequences. The study proposes a data-driven diagnostic support system that improves efficiency over time-consuming traditional assessments. Using 768 records with class imbalance, it combines feature selection, dimensionality reduction, and grid search–based optimization. The Extra Trees model tuned via grid search achieves strong results, including 92.5% accuracy, 93.7% F1-score, and 92.47% AUC-ROC, highlighting machine learning’s value for more reliable diabetes detection and improved healthcare outcomes.","JOE International Journal of  \nOnline and Biomedical Engineering  \n[Onli](Online-Journals.org)[ne-Jo](Online-Journals.org)[urnals](Online-Journals.org)[.org](Online-Journals.org)  \niJOE | eISSN: 2626-8493 | Vol. 20 No. 8 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i08.47765](https://doi.org/10.3991/ijoe.v20i08.47765)  \nPAPER  \nDiabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction  \nAbd Allah Aouragh1(*), Mohamed Bahaj1, Fouad Toufik2  \n1MIET Laboratory, Faculty of Sciences and Techniques, Hassan 1st University, Settat, Morocco  \n2Computer Sciences Laboratory, Higher School of Technology, Mohammed V University, Sale, Morocco  \n[a.aouragh@uhp.ac.ma](a.aouragh@uhp.ac.ma)  \nABSTRACT  \nDiabetes, a pervasive global health concern, presents diagnostic challenges due to its nuanced onset and far-reaching implications. Traditional diagnostic approaches, reliant on time-consuming assessments, necessitate a paradigm shift towards more efficient methodologies. In response, this study introduces a diagnostic support system leveraging the power of optimized machine learning algorithms. Addressing class imbalance within a dataset comprising 768 records, our methodology intricately weaves together feature selection, dimensionality reduction techniques, and grid search optimization. Specifically, the Extra Trees model, fine-tuned via grid search, emerges as the most potent, showcasing remarkable performance metrics: an accuracy score of 92.5%, an F1-score of 93.7%, and an AUC-ROC of 92.47% . These findings underscore the pivotal role of machine learning in reshaping diabetes diagnosis, offering transformative possibilities for global healthcare enhancement.  \nKEYWORDS  \ndiabetes, machine learning, balancing, feature selection, dimensionality reduction, grid search  \n1 INTRODUCTION  \nDiabetes is a persistent health disorder that manifests when there is deficient insulin secretion from the pancreas or when the body faces challenges in utilizing the insulin it generates efficiently. Insulin plays a crucial role in managing blood sugar levels. Uncontrolled diabetes often leads to hyperglycemia, which is marked by high blood glucose levels [1, 2] .  \nAccording to the latest statistics released by the World Health Organization (WHO), diabetes has become a serious metabolic challenge, spreading on a global scale and generating growing public awareness [2] . The proportion of people affected by this disease has climbed dramatically, from 108 million in 1980 to an alarming 422 million in 2014 [2] .  \nThe nuanced manifestations of diabetes, including heightened thirst, recurrent urination, enduring fatigue, and unexplained weight loss, are indicative of  \nAouragh, A.A., Bahaj, M., Toufik, F. (2024) . Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction. International Journal of Online and Biomedical Engineering (iJOE), 20(8), pp. 100–114. [https://doi.org/10.3991/ijoe.v20i08.47765](https://doi.org/10.3991/ijoe.v20i08.47765)[ ](https://doi.org/10.3991/ijoe.v20i08.47765)[Article submitted 2024-01-05. Revision uploaded 2024-02-22. Final acceptance 2024-02-23.](Article submitted 2024-01-05. Revision uploaded 2024-02-22. Final acceptance 2024-02-23.)  \n© 2024 by the authors of this article. Published under CC-BY.  \n100 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 8 (2024)  \nDiabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction  \nunderlying disruptions in carbohydrate metabolism [1, 2] . Despite being frequently overlooked, these initial warning signs serve as harbingers of potential severe complications. In certain instances, they may escalate into cardiovascular diseases, renal impairments, ocular complications and in more extreme cases, necessitate lower-limb amputations [2]. It is crucial to recognize and address these early indicators promptly, as the","cbCaicaHsutO9NXa","https://ap.wps.com/l/cbCaicaHsutO9NXa","pdf",580725,1,15,"English","en",105,"# Introduction\n## Diabetes overview and diagnostic challenges\n## Machine learning for improved early detection\n# Methodology\n## Dataset and class imbalance handling\n## Feature selection and dimensionality reduction\n## Grid search optimization and model tuning\n# Results\n## Performance evaluation (accuracy, F1-score, AUC-ROC)\n# Conclusion\n## Impact on diabetes diagnosis and healthcare support","[{\"question\":\"What problem does the study address in diabetes diagnosis?\",\"answer\":\"The study targets the diagnostic difficulty of diabetes and the inefficiency of traditional, time-consuming assessments by introducing an optimized machine-learning–based support system.\"},{\"question\":\"How does the methodology handle the dataset imbalance?\",\"answer\":\"It uses a dataset of 768 records and incorporates feature selection and dimensionality reduction together with grid search optimization, explicitly addressing class imbalance in model training.\"},{\"question\":\"Which machine learning model performed best and what were its results?\",\"answer\":\"The Extra Trees model, fine-tuned with grid search, delivered the strongest performance, reaching 92.5% accuracy, 93.7% F1-score, and 92.47% AUC-ROC.\"}]","Diabetes Prediction: Optimization of Machine Learning through Feature Selection and Dimensionality Reduction | 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problem does the study address in diabetes diagnosis?","Question",{"text":75,"@type":76},"The study targets the diagnostic difficulty of diabetes and the inefficiency of traditional, time-consuming assessments by introducing an optimized machine-learning–based support system.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the methodology handle the dataset imbalance?",{"text":80,"@type":76},"It uses a dataset of 768 records and incorporates feature selection and dimensionality reduction together with grid search optimization, explicitly addressing class imbalance in model training.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning model performed best and what were its results?",{"text":84,"@type":76},"The Extra Trees model, fine-tuned with grid search, delivered the strongest performance, reaching 92.5% accuracy, 93.7% F1-score, and 92.47% 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