[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122149-en":3,"doc-seo-122149-105":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},122149,687197207639,"Asher","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Multi-Label Risk Prediction Diabetes Complication Using Machine Learning Models","Early diagnosis of diabetic complications based on risk factors is essential but remains understudied, particularly in the context of multi-label classification (MLC). This study uses BRFSS data from 2016 to 2021 to classify seven diabetes complications through MLC combined with multiple machine learning models. Thirty-three variables per year were analyzed after statistical assessment and preprocessing. Seven models (ANN, RF, DT, K-NN, NB, SVM, and DNN) were compared across two MLC frameworks: problem transformation and algorithm adaptation.","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. 16 (2024) |   \n[https://doi.org/10.3991/ijoe.v20i16.51643](https://doi.org/10.3991/ijoe.v20i16.51643)  \nPAPER  \nMulti-Label Risk Prediction Diabetes Complication Using Machine Learning Models  \nNur Rachman Dzakiyullah1–3(􀀍),  \nM. A. Burhanuddin3, Raja Rina Raja Ikram3, Novanto Yudistira4, Muhammad Rifqi Fauzi4, Dwi Joko Purbohadi5  \n1Faculty of Computer and Engineering, Department of Information System, Universitas Alma Ata, Yogyakarta, Indonesia  \n2Alma Ata Center for Medical Informatics, Universitas Alma Ata, Yogyakarta, Indonesia  \n3Faculty of Information & Communication Technology, Universiti Teknikal Malaysia Melaka, Durian Tunggal, Melaka  \n4Faculty of Computer Science, Universitas Brawijaya, Malang, Indonesia  \n5Faculty Engineering, Department of Information Technology, Universitas Muhammadiyah Yogyakarta, Yogyakarta, Indonesia  \nnurrachmandzakiyullah@ [almaata.ac.id](almaata.ac.id)  \nABSTRACT  \nEarly diagnosis of diabetic complications based on risk factors is essential but remains understudied, particularly in the context of multi-label classification (MLC). This study leverages data from the behavioral risk factor surveillance system (BRFSS) from 2016 to 2021 to classify seven diabetes complications using MLC techniques combined with multiple machine learning (ML) models. We analyzed 33 variables per dataset year after thorough statistical analysis and preprocessing. Seven ML models were employed: Artificial neural network (ANN), random forest (RF), decision tree (DT), K-nearest neighbors (K-NN), naïve Bayes (NB), support vector machine (SVM), and deep neural network (DNN) . We compared two MLC frameworks: problem transformation and algorithm adaptation. The performance of the models was evaluated using several metrics, and feature importance for each complication was analyzed. Our results indicate that the algorithm adaptation framework, particularly with DNN models, outperforms problem transformation. This highlights the potential of this approach for improving classification performance in complex diseases with multiple complications.  \nKEYWORDS  \nmulti-label classification, risk prediction models, diabetes complication, machine learning (ML), early diagnosis  \n1 INTRODUCTION  \nDiabetes is a metabolic disease that can cause complications affecting vital organs of the human body, classified as either microvascular or macrovascular complications, along with associated comorbidities [1] . These complications arise when blood sugar is not adequately controlled, particularly in patients who fail to manage their risk factors through medication or lifestyle changes. According to the international diabetes federation (IDF), 1 in 10 adults aged 20–79 years have diabetes, with projections indicating that the number will rise to 643 million by 2030 and 783 million by 2045 [2] . Additionally, unhealthy lifestyle behaviors, driven by social, economic,  \nDzakiyullah, N. R., Burhanuddin, M.A., Raja Ikram, R. R., Yudistira, N., Fauzi, M. R., Purbohadi, D.J. (2024) . Multi-Label Risk Prediction Diabetes Complication Using Machine Learning Models. International Journal of Online and Biomedical Engineering (iJOE), 20(16), pp. 66–88. [https://doi.org/](https://doi.org/)[ ](https://doi.org/)10.3991/ijoe.v20i16.51643  \nArticle submitted 2024-08-08. Revision uploaded 2024-10-08. Final acceptance 2024-10-08.  \n© 2024 by the authors of this article. Published under CC-BY.  \n66 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 20 No. 16 (2024)  \nMulti-Label Risk Prediction Diabetes Complication Using Machine Learning Models  \nor interpersonal pressures, are linked to diabetes risk [3]–[5] . Even in high-income countries such as the United States, the financial burden of diabetes is significant. Th","cbCaia2iGsQkzhBX","https://ap.wps.com/l/cbCaia2iGsQkzhBX","pdf",777763,1,23,"English","en",105,"# 1 Introduction\n# 2 Related Work\n# 3 Methodology\n## Data Source and Preprocessing\n## Multi-Label Learning Frameworks\n## Machine Learning Models\n## Evaluation Metrics and Feature Importance\n# 4 Results and Discussion\n# 5 Conclusion","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To predict the risk of seven diabetes complications by using multi-label classification with multiple machine learning models and comparing two MLC frameworks.\"},{\"question\":\"Which dataset and time range are used?\",\"answer\":\"The study leverages the BRFSS behavioral risk factor surveillance system data from 2016 to 2021.\"},{\"question\":\"How do the two multi-label learning frameworks compare?\",\"answer\":\"The algorithm adaptation framework, especially with DNN models, shows better performance than problem transformation.\"}]","Multi-Label Risk Prediction Diabetes Complication Using Machine Learning Models | PDF",1785809074,58,{"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},"multi-label-risk-prediction-diabetes-complication-using-machine-learning-models","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/multi-label-risk-prediction-diabetes-complication-using-machine-learning-models/122149/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To predict the risk of seven diabetes complications by using multi-label classification with multiple machine learning models and comparing two MLC frameworks.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which dataset and time range are used?",{"text":80,"@type":76},"The study leverages the BRFSS behavioral risk factor surveillance system data from 2016 to 2021.",{"name":82,"@type":73,"acceptedAnswer":83},"How do the two multi-label learning frameworks compare?",{"text":84,"@type":76},"The algorithm adaptation framework, especially with DNN models, shows better performance than problem transformation.","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,120,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]