[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126677-en":3,"doc-seo-126677-105":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},126677,962084925502,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_6f874abed73319feea01a86fa6f0fab8",8,"Research & Report","A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction","Early detection of diabetes is essential because the disease is incurable and can rapidly lead to severe complications. Existing machine learning models differ in performance across multiple accuracy measures, making technique selection difficult. The paper introduces a multicriteria decision-making (MCDM) framework to evaluate and fuse rankings of machine learning techniques for diabetes prediction using WSM, TOPSIS, and VIKOR. Experiments use 10 techniques on the Pima dataset with eight metrics and recommend logistic regression as a suitable modeling choice.","International Journal of Engineering and Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nA Novel MCDM-Based Framework to Recommend Machine Learning  \nTechniques for Diabetes Prediction  \nAjay Kumar 1,*, Kamaldeep Kaur2  \n1Department of IT, KIET Group of Institutions, Delhi-NCR, Ghaziabad, Uttar Pradesh, India 2USIC&T, Guru Gobind Singh Indraprastha University, New Delhi, India  \nReceived 27 March 2023; received in revised form 04 June 2023; accepted 16 June 2023  \nDOI: [https://doi.org/10.46604/ijeti.2023.11837](https://doi.org/10.46604/ijeti.2023.11837)  \nAbstract  \nEarly detection of diabetes is crucial because of its incurable nature. Several diabetes prediction models have been developed using machine learning techniques (MLTs). The performance of MLTs varies for different accuracy measures. Thus, selecting appropriate MLTs for diabetes prediction is challenging. This paper proposes a multicriteria decision-making (MCDM) based framework for evaluating MLTs applied to diabetes prediction. Initially, three MCDM methods—WSM, TOPSIS, and VIKOR—are used to determine the individual ranks of MLTs for diabetes prediction performance by using various comparable performance measures (PMs). Next, a fusion approach is used to determine the final rank of the MLTs. The proposed method is validated by assessing the performance of 10 MLTs on the Pima Indian diabetes dataset using eight evaluation metrics for diabetes prediction. Based on the final MCDM rankings, logistic regression is recommended for diabetes prediction modeling.  \nKeywords: diabetes prediction, machine learning techniques, WSM, TOPSIS, VIKOR  \n1. Introduction  \nDiabetes is among the most prevalent and severe health conditions worldwide. According to the International Diabetes Federation [1], 537 million individuals globally have diabetes. This number is projected to increase to 643 million by 2030. Over 18% of global deaths can be attributed to four primary diseases, namely chronic respiratory diseases, cancer, cardiovascular disease, and diabetes, emphasizing their significance as major public health concerns [2]. Obesity, poor dietary habits, elevated blood pressure, genetic predisposition to diabetes, advanced age, physical inactivity, and lifestyle factors all contribute to diabetes. As the disease progresses, patients with diabetes are more likely to develop health complications, such as heart disease, nerve damage, stroke, kidney failure, and vision problems.  \nSimilar to many diseases, early diagnosis of diabetes is pivotal for managing diabetes and preventing its progression and severe symptoms. Diabetes is typically diagnosed either manually by medical professionals or through technology-driven methods. Each of these procedures has unique advantages and disadvantages. Although manual diagnosis by medical practitioners offers exceptional human insight, technological advancements have significantly improved this procedure, making it the predominant choice currently [1] . Technology-based approaches have the advantage of requiring less time and resources. In addition, in the early stage of the disease, technology can more efficiently identify the signs of diabetes than manual procedures while avoiding human error and complications. With the increasing availability of electronic health records, automated diabetes detection technologies have become increasingly appealing.  \n* Corresponding author. E-mail [address: ajaygarg100@gmail.com](address: ajaygarg100@gmail.com)  \n[English language proofreader: Chih-Wen Teng](English language proofreader: Chih-Wen Teng)  \n2 International Journal of Engineering and Technology Innovation, vol. x, no. x, 20xx, pp. xx-xx  \nStudies have recently examined the performance of machine learning–based models in diabetes prediction and demonstrated their benefits in the healthcare industry [3-4] . Such models are widely used to predict not only diabetes but also other diseases. For example, Hussain et al. [5] examined the accuracy ","cbCaieLkhvpRdnee","https://ap.wps.com/l/cbCaieLkhvpRdnee","pdf",3336314,1,15,"English","en",105,"# 1. Introduction\n## Diabetes and the need for early detection\n## Challenges in selecting machine learning techniques\n## Study objective and proposed approach","[{\"question\":\"Why is early detection of diabetes important?\",\"answer\":\"Early detection helps manage diabetes and prevents progression to severe symptoms and complications. The paper highlights diabetes’s incurable nature and the public health burden.\"},{\"question\":\"What problem does the proposed MCDM framework address?\",\"answer\":\"It addresses the difficulty of selecting suitable machine learning techniques because model performance varies across different evaluation metrics.\"},{\"question\":\"How are final rankings of machine learning techniques determined?\",\"answer\":\"Individual rankings are computed using WSM, TOPSIS, and VIKOR based on multiple comparable performance measures, then a fusion method (rank position method, RPM) produces the final ranking.\"}]","A Novel MCDM-Based Framework to Recommend Machine Learning Techniques for Diabetes Prediction | PDF",1785934181,38,{"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-novel-mcdm-based-framework-to-recommend-machine-learning-techniques-for-diabetes-prediction","",{"@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/a-novel-mcdm-based-framework-to-recommend-machine-learning-techniques-for-diabetes-prediction/126677/",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","2026-08-05",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},"Why is early detection of diabetes important?","Question",{"text":75,"@type":76},"Early detection helps manage diabetes and prevents progression to severe symptoms and complications. The paper highlights diabetes’s incurable nature and the public health burden.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What problem does the proposed MCDM framework address?",{"text":80,"@type":76},"It addresses the difficulty of selecting suitable machine learning techniques because model performance varies across different evaluation metrics.",{"name":82,"@type":73,"acceptedAnswer":83},"How are final rankings of machine learning techniques determined?",{"text":84,"@type":76},"Individual rankings are computed using WSM, TOPSIS, and VIKOR based on multiple comparable performance measures, then a fusion method (rank position method, RPM) produces the final ranking.","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"]