[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124840-en":3,"doc-seo-124840-105":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":4,"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},124840,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features","Diabetes is a global health challenge where differences and uncertainties in human lifestyles make risk patterns difficult to generalize. Risk likelihood is commonly estimated with machine learning using explicit dataset features, yet the intrinsic relationships among features are often overlooked. This study derives top feature pairs via feature importance and correlation from anonymized patient data (263,882 samples), then evaluates five ML models on both correlated pairs and direct features, using accuracy, precision, recall, and F1-score. Neural networks achieve the best F1-scores: 85% for correlated pairs and 75% for direct features.","[Comp. Open 2024.02. Downloaded from www.worldscientific.com](Comp. Open 2024.02. Downloaded from www.worldscientific.com)[ ](Comp. Open 2024.02. Downloaded from www.worldscientific.com)by 86.27.28.203 on 07 except Open Access articles/04/24. Re-use and distribution is strictly not permitted, for .  \n OPEN ACCESS  \nComputing Open  \nVol. 2 (2024) 2450006 (13 pages)\\# The Author(s)  \nDOI: 10.1142/S2972370124500065  \nMachine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features  \nAyodeji O. J. Ibitoye *  \nSchool of Computing and Mathematical Sciences  \nUniversity of Greenwich, SE10 9LS, London, United Kingdom  \n[a.o.ibitoye@greenwich.ac.uk](a.o.ibitoye@greenwich.ac.uk)  \nJoseph D. Akinyemi   \nDepartment of Computer Science  \nUniversity of York, YO10 5DD, York, United Kingdom  \n[joseph.akinyemi@york.ac.uk](joseph.akinyemi@york.ac.uk)  \n[Olufade F. W. Onifade](Olufade F. W. Onifade)   \nDepartment of Computer Science  \nUniversity of Ibadan, Nigeria  \n[ofw.onifade@ui.edu.ng](ofw.onifade@ui.edu.ng)  \nReceived 29 November 2023  \nRevised 4 May 2024  \nAccepted 13 May 2024  \nPublished 12 June 2024  \nDiabetes is a global health concern that a®ects people of all races. With di®erent uncertainties inhuman lifestyles, it is di±cult to predict diabetes while assuming that the risk patterns are the same for all. The likelihood of diabetes in a patient is mostly predicted using machine learning (ML) models on features explicitly available in datasets, while the intrinsic relationship between features viz-a-viz their potential relevance to the presence of diabetes is oftentimes neglected. In this work, we explored feature importance and correlation to derive the top 15 feature pairs from a dataset of 263,882 samples of anonymized patient information. These top-15 feature pairs were fed into ¯ve di®erent ML models (decision tree (DT), neural networks (NN), random forest (RF), support vector machine (SVM) and extreme gradient boosting (XGB)) for predicting the likelihood of diabetes, while also feeding the direct features (without correlated pairing) separately into the same 5ML models. The models' performances were evaluated using accuracy, precision, recall and F1-score and NN presented the best performance overall achieving an F1-score of 85% for the correlated feature pairs (CF) and 75% for the direct feature pairs.  \n* Corresponding author.  \nThis is an Open Access article published by World Scienti¯c Publishing Company. It is distributed under the terms of the Creative Commons Attribution 4.0 (CC BY) License which permits use, distribution and reproduction in any medium, provided the original work is properly cited.  \n[Comp. Open 2024.02. Downloaded from www.worldscientific.com](Comp. Open 2024.02. Downloaded from www.worldscientific.com)[ ](Comp. Open 2024.02. Downloaded from www.worldscientific.com)by 86.27.28.203 on 07 except Open Access articles/04/24. Re-use and distribution is strictly not permitted, for .  \nA. O. J. Ibitoye, J. D. Akinyemi & O. F. W. Onifade  \nThe results con¯rm the importance of the correlation/relationship between features in predicting the likelihood of diabetes in patients more accurately.  \nKeywords: Diabetes; machine learning; risk prediction; paired relationship; decision support.  \n1. Introduction  \nDiabetes is a medical ailment that is depicted by high quantities of blood glucose or sugar in the human body. With two basic types of diabetes, health experts explained that Type 1 diabetes is initiated by a de¯ciency in insulin generation by the pancreas, and Type 2 diabetes is characterized by the body's cells becoming de¯ant to the results of insulin.1 Despite being a non-communicable disease, type 2 diabetes has recently gained the status of an epidemic silent killer.2 It is not a respect of age, ethnicity, or nationality. If a person's blood sugar balance varies from 100 to 125 mg/dL, he is diagnosed with prediabetes since the actual normal range of glucose levels in the human body is 70","cbCaikwy70TK4o5L","https://ap.wps.com/l/cbCaikwy70TK4o5L","pdf",649613,1,13,"English","en",105,"# Introduction\n## Diabetes background and challenge of early detection\n## Data mining and machine learning for diabetes prediction\n## Feature engineering for risk modeling","[{\"question\":\"Why is diabetes risk prediction difficult when using generic risk patterns?\",\"answer\":\"Differences and uncertainties in human lifestyles cause risk patterns to vary, making it hard to predict diabetes reliably across all individuals.\"},{\"question\":\"How does the study incorporate feature relationships into diabetes prediction?\",\"answer\":\"It computes feature importance and correlation to extract the top 15 feature pairs, then feeds these correlated pairs into five machine learning models.\"},{\"question\":\"Which model performed best and how did it compare between correlated pairs and direct features?\",\"answer\":\"Neural networks delivered the highest overall performance, with an F1-score of 85% for correlated feature pairs versus 75% for direct (unpaired) features.\"}]","Machine Learning-Based Diabetes Risk Prediction Using Associated Behavioral Features | 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is diabetes risk prediction difficult when using generic risk patterns?","Question",{"text":75,"@type":76},"Differences and uncertainties in human lifestyles cause risk patterns to vary, making it hard to predict diabetes reliably across all individuals.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study incorporate feature relationships into diabetes prediction?",{"text":80,"@type":76},"It computes feature importance and correlation to extract the top 15 feature pairs, then feeds these correlated pairs into five machine learning models.",{"name":82,"@type":73,"acceptedAnswer":83},"Which model performed best and how did it compare between correlated pairs and direct features?",{"text":84,"@type":76},"Neural networks delivered the highest overall performance, with an F1-score of 85% for correlated feature pairs versus 75% for direct (unpaired) 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