[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117985-en":3,"doc-seo-117985-105":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},117985,4810365810221,"Aurora","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",7,"Healthcare","Early Diagnosis of Diabetes - A Comparison of Machine Learning Methods","Early diagnosis and timely management of diabetes are crucial as the disease rapidly becomes a global health crisis. This study evaluates machine learning–based predictions of diabetes onset using a Pima Indians dataset containing ages, body mass indexes, and glucose levels for 768 patients. Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network are compared using class-weighted performance. Results show Logistic Regression and Neural Network achieve the strongest overall performance, while kNN and Tree models score lower.","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. 19 No. 15 (2023) |   \n[https://doi.org/10.3991/ijoe.v19i15.42417](https://doi.org/10.3991/ijoe.v19i15.42417)  \nPAPER  \nEarly Diagnosis of Diabetes: A Comparison of Machine Learning Methods  \nMowafaq Salem Alzboon1(􀀍), Mohammad Subhi Al-Batah1, Muhyeeddin Alqaraleh1, Ahmad Abuashour2, Ahmad Fuad Hamadah Bader3  \n1Faculty of Science and Information Technology, Jadara University, Irbid, Jordan  \n2Faculty of Computer Studies, Arab Open University,  \nAl-Ardiya, Kuwait  \n3Faculty of Engineering, Jadara University, Irbid, Jordan  \n[malzboon@jadara.edu.jo](malzboon@jadara.edu.jo)  \nABSTRACT  \nDetection and management of diabetes at an early stage is essential since it is rapidly becoming a global health crisis in many countries. Predictions of diabetes using machine learning algorithms have been promising. In this work, we use data collected from the Pima Indians to assess the performance of multiple machine-learning approaches to diabetes prediction. Ages, body mass indexes, and glucose levels for 768 patients are included in the data set. The methods evaluated are Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network. The findings indicate that the Logistic Regression and Neural Network models perform the best on most criteria when considering all classes together. The SVM, Random Forest, and Naive Bayes models also receive moderate to high scores, suggesting their strength as classification models. However, the kNN and Tree models show poorer scores on most criteria across all classes, making them less favorable choices for this dataset. The SGD, AdaBoost, and CN2 rule inducer models perform the poorest when comparing all models using a weighted average of class scores. The results of the study suggest that machine learning algorithms may help predict the onset of diabetes and for detecting the disease at an early stage.  \nKEYWORDS  \ndiabetes, decision trees, machine learning, diagnosis, support vector  \n1 INTRODUCTION  \nMillions of people worldwide have diabetes, a chronic metabolic disease that is a leading cause of illness and mortality [1] . Complications of diabetes include high blood sugar, which can lead to heart disease, stroke, blindness, and even amputations. It is crucial to diagnose and treat diabetes as soon as possible to reduce the risk of complications, but this can be difficult because the disease often presents with vague or nonexistent symptoms [2] . To produce inferences or predictions from data without being explicitly programmed is the goal of machine learning (ML), a branch of artificial intelligence. Algorithms based on machine learning have several  \nAlzboon, M.S., Al-Batah, M.S., Alqaraleh, M., Abuashour, A., Hamadah Bader, A.F. (2023). Early Diagnosis of Diabetes: A Comparison of Machine Learning Methods. International Journal of Online and Biomedical Engineering (iJOE), 19(15), pp. 144–165. [https://doi.org/10.3991/ijoe.v19i15.42417](https://doi.org/10.3991/ijoe.v19i15.42417)[ ](https://doi.org/10.3991/ijoe.v19i15.42417)[Article submitted 2023-06-19. Revision uploaded 2023-08-05. Final acceptance 2023-08-13.](Article submitted 2023-06-19. Revision uploaded 2023-08-05. Final acceptance 2023-08-13.)  \n© 2023 by the authors of this article. Published under CC-BY.  \n144 International Journal of Online and Biomedical Engineering (iJOE) iJOE | Vol. 19 No. 15 (2023)  \nEarly Diagnosis of Diabetes: A Comparison of Machine Learning Methods  \napplications, one being healthcare. The likelihood of a patient acquiring diabetes may be predicted from patient data using ML algorithms [3] . Motivation: Finding the best machine learning algorithm for diabetes prediction is the goal of this research. Diagnosis and treatment ","cbCaio8c8ogzilWa","https://ap.wps.com/l/cbCaio8c8ogzilWa","pdf",372987,1,22,"English","en",105,"# Abstract\n# Introduction\n# Literature Review","[{\"question\":\"Why is early diagnosis of diabetes important in this study?\",\"answer\":\"Early detection supports faster intervention and reduces the severity of the disease. The paper highlights that diabetes complications can arise even when symptoms are vague or absent.\"},{\"question\":\"Which machine learning models were evaluated for diabetes prediction?\",\"answer\":\"The study compares Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network.\"},{\"question\":\"What conclusion does the study reach about the best-performing algorithms?\",\"answer\":\"Logistic Regression and Neural Network perform best overall on most criteria when considering all classes together. SVM, Random Forest, and Naive Bayes show moderate to high scores, while kNN and Tree models perform poorer.\"}]","Early Diagnosis of Diabetes - A Comparison of Machine Learning Methods | PDF",1785680644,55,{"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},"early-diagnosis-of-diabetes-a-comparison-of-machine-learning-methods","",{"@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/early-diagnosis-of-diabetes-a-comparison-of-machine-learning-methods/117985/",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","2026-08-02",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 diagnosis of diabetes important in this study?","Question",{"text":75,"@type":76},"Early detection supports faster intervention and reduces the severity of the disease. The paper highlights that diabetes complications can arise even when symptoms are vague or absent.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models were evaluated for diabetes prediction?",{"text":80,"@type":76},"The study compares Logistic Regression, Decision Tree, Random Forest, k-Nearest Neighbors, Naive Bayes, Support Vector Machine, Gradient Boosting, and Neural Network.",{"name":82,"@type":73,"acceptedAnswer":83},"What conclusion does the study reach about the best-performing algorithms?",{"text":84,"@type":76},"Logistic Regression and Neural Network perform best overall on most criteria when considering all classes together. SVM, Random Forest, and Naive Bayes show moderate to high scores, while kNN and Tree models perform poorer.","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},[93,97,101,105,110,115,118,123,128,131,135],{"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":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",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"]