[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123101-en":3,"doc-seo-123101-105":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},123101,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",7,"Healthcare","A Multiclass Approach to Predicting Diabetes Using Machine Learning","The study addresses diabetes mellitus as a major global public health challenge by developing machine learning models to predict diabetes stage. It uses dimensionality reduction combined with multiple ML classifiers, leveraging a Centers for Disease Control and Prevention dataset with 253,680 instances and 23 features. The approach incorporates both medical indicators and social determinants of health. Model quality is evaluated with accuracy, precision, recall, F1 score, ROC, AUC, and balanced accuracy, showing Logistic Regression and XGBoost achieving 85% accuracy.","Association for Information Systems  \nAIS Electronic Library (AISeL)  \n\n| ACIS 2023 Proceedings | Australasian (ACIS) |\n| --- | --- |\n| 12-2-2023\u003Cbr>A Multiclass Approach to Predicting Diabetes Using Learning\u003Cbr>Emmanuel Mbuya\u003Cbr>University of Johannesburg, South Africa, [Emmechcool@gmail.com](Emmechcool@gmail.com)\u003Cbr>Tsholofelo Mokheleli\u003Cbr>University of Johannesburg, South Africa, [mokhelelitsholo48@gmail.com](mokhelelitsholo48@gmail.com)\u003Cbr>Tebogo Bokaba\u003Cbr>University of Johannesburg, South Africa, [tbokaba@uj.ac.za](tbokaba@uj.ac.za)\u003Cbr>Patrick Ndayizigamiye\u003Cbr>University of Johannesburg, South Africa, [ndayizigamiyep@uj.ac.za](ndayizigamiyep@uj.ac.za)\u003Cbr>Follow this and additional works at: [https://aisel.aisnet.org/acis2023](https://aisel.aisnet.org/acis2023) | Machine |\n\nRecommended Citation  \nMbuya, Emmanuel; Mokheleli, Tsholofelo; Bokaba, Tebogo; and Ndayizigamiye, Patrick, \"A Multiclass Approach to Predicting Diabetes Using Machine Learning\" (2023) . ACIS 2023 Proceedings. 140.  \n[https://aisel.aisnet.org/acis2023/140](https://aisel.aisnet.org/acis2023/140)  \nThis material is brought to you by the Australasian (ACIS) at AIS Electronic Library (AISeL) . It has been accepted for inclusion in ACIS 2023 Proceedings by an authorized administrator of AIS Electronic Library (AISeL) . For more information, please [contact elibrary@aisnet.org](contact elibrary@aisnet.org).  \nAustralasian Conference on Information Systems 2023, Wellington  \nMbuya et al. Predicting diabetes stage  \nA Multiclass Approach to Predicting Diabetes Stage Using Machine Learning  \nFull research paper  \nEmmanuel Mbuya  \nDepartment of Applied Information Systems  \nUniversity of Johannesburg  \nJohannesburg, South Africa  \nEmail: [emmechcool@gmail.com](emmechcool@gmail.com)  \nTsholofelo Diphoko Mokheleli  \nDepartment of Applied Information Systems  \nUniversity of Johannesburg  \nJohannesburg, South Africa  \n[Email: mokhelelitsholo48@gmail.com](Email: mokhelelitsholo48@gmail.com)  \nTebogo Bokaba  \nDepartment of Applied Information Systems  \nUniversity of Johannesburg  \nJohannesburg, South Africa  \nEmail: [tbokaba@uj.ac.za](tbokaba@uj.ac.za)  \nPatrick Ndayizigamiye  \nDepartment of Applied Information Systems  \nUniversity of Johannesburg  \nJohannesburg, South Africa  \n[Email: ndayizigamiyep@uj.ac.za](Email: ndayizigamiyep@uj.ac.za)  \nAbstract  \nThe global prevalence of diabetes mellitus poses a significant public health challenge. This study aims to use dimensionality reduction methods with machine learning (ML) algorithms to predict the diabetes stage and assess the performance of the developed predictive model. Unlike many studies on predicting diabetes, this study makes use of both medical indicators and social determinants of health to predict the risk of diabetes. Utilizing a large dataset obtained from the Centers for Disease Control and Prevention, comprising 253,680 instances and 23 features, this study employs various ML algorithmsand dimensionality reduction techniques. In addition, the study applied several metrics namely accuracy, precision, recall, F1 score, Receiver Operating Characteristic, Area Under the Curve, and balanced accuracy. The study finds that Logistic Regression and XGBoost models outperform other classifiers, achieving an accuracy of 85% . The study suggests that future work could benefit from incorporating deep learning techniques.  \nKeywords Cross-validation, Diabetes Mellitus, Dimensionality Reduction, Machine Learning.  \nAustralasian Conference on Information Systems 2023, Wellington  \nMbuya et al. Predicting diabetes stage  \n1 Introduction  \nDiabеtеs mеllitus also referred to as diabеtеs, is a chronic illnеss dеfinеd by elevated blood glucosе lеvеlsor signs of hyperglycemia caused by partial or complete insulin deficiency (Banday et al. 2020) . Diabetes is a long-term medical condition that requires rigorous compliance with a treatment plan. This entails adherence to a nutrition plan, a healthy lifestyle, sеlf-monitoring of bl","cbCailpgiYQY6vYk","https://ap.wps.com/l/cbCailpgiYQY6vYk","pdf",676746,1,12,"English","en",105,"# Abstract\n# Introduction","[{\"question\":\"What is the goal of the study on diabetes prediction?\",\"answer\":\"The study aims to predict the diabetes stage using machine learning while assessing the performance of the developed predictive model.\"},{\"question\":\"Which data sources and features are used?\",\"answer\":\"The work uses a large CDC dataset with 253,680 instances and 23 features, combining medical indicators with social determinants of health.\"},{\"question\":\"Which machine learning models performed best and how was performance measured?\",\"answer\":\"Logistic Regression and XGBoost outperformed other classifiers, reaching about 85% accuracy. Performance is measured using accuracy, precision, recall, F1 score, ROC/AUC, and balanced accuracy.\"}]","A Multiclass Approach to Predicting Diabetes Using Machine Learning | PDF",1785814647,30,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":86,"head_meta":88,"extra_data":90,"updated_unix":28},"a-multiclass-approach-to-predicting-diabetes-using-machine-learning","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/a-multiclass-approach-to-predicting-diabetes-using-machine-learning/123101/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the goal of the study on diabetes prediction?","Question",{"text":75,"@type":76},"The study aims to predict the diabetes stage using machine learning while assessing the performance of the developed predictive model.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which data sources and features are used?",{"text":80,"@type":76},"The work uses a large CDC dataset with 253,680 instances and 23 features, combining medical indicators with social determinants of health.",{"name":82,"@type":73,"acceptedAnswer":83},"Which machine learning models performed best and how was performance measured?",{"text":84,"@type":76},"Logistic Regression and XGBoost outperformed other classifiers, reaching about 85% accuracy. 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