[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124916-en":3,"doc-seo-124916-105":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},124916,4398048950312,"Violet","https://ap-avatar.wpscdn.com/avatar/400002538284de19e3c?_k=1778320343897328908",8,"Research & Report","Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms - Abstract, Introduction and Objectives","Diabetes mellitus (DM) is a rapidly increasing global metabolic disorder characterized by persistently elevated blood glucose levels. When undiagnosed, it can trigger serious complications such as retinopathy, nephropathy, neuropathy, and other vascular abnormalities. Machine learning methods are positioned for early identification, diagnosis, and therapy monitoring. The study compares multiple classifiers—SVM, Naïve Bayes, KNN, random forest, logistic regression, and decision tree—on Pima Indian and Germany datasets using WEKA 3.8.6.","Journal of Advanced Zoology  \nISSN: 0253-7214  \nVolume 45 IssueS-4 Year 2024 Page 465-477  \nComparative Analysis of Diabetic Prediction Using Machine Learning Algorithms  \nMs. Madhuvanthi B1*, Dr. Baskaran T S2  \n*1Research Scholar, PG & Research Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi-613503, Thanjavur,“Affiliated to Bharathidasan University, Tiruchirappalli-620024”, Tamil Nadu, [India. E-Mail: madhuvanthib@yahoo.in](India. E-Mail: madhuvanthib@yahoo.in)  \n2Associate Professor & Research Supervisor,  \nPG & Research Department of Computer Science,  \nA Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi-613503, Thanjavur,“Affiliated to Bharathidasan University, Tiruchirapalli-620024”, TamilNadu, India.  \nE-Mail: [t_s_baskaran@yahoo.com](t_s_baskaran@yahoo.com)  \n*Corresponding Author: Ms. Madhuvanthi B  \n*Research Scholar, PG & Research Department of Computer Science, A. Veeriya Vandayar Memorial Sri Pushpam College (Autonomous), Poondi-613503, Thanjavur,“Affiliated to Bharathidasan University,  \nTiruchirappalli-620024”, Tamil Nadu, [India. E-Mail: madhuvanthib@yahoo.in](India. E-Mail: madhuvanthib@yahoo.in)  \n\n| CC License\u003Cbr>CC-BY-NC-SA 4.0 | Abstract\u003Cbr>Diabetes mellitus (DM) is a severe worldwide health problem, and its prevalence is quickly growing. It is a spectrum of metabolic illnesses definite by continually increased blood glucose levels. Undiagnosed diabetes can lead to a variety of difficulties, including retinopathy, nephropathy, neuropathy, and other vascular abnormalities. In this context, machine learning (ML) technologies may be mainly useful for early disease identification, diagnosis, and therapy monitoring. The core idea of this study is to detect the strong ML algorithm to forecast it. For this numerous ML algorithms were chosen i.e., support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT), according to this work. Two, Pima Indian diabetic (PID) and Germany diabetes datasets were used and the research was implemented using Waikato environment for knowledge analysis (WEKA) 3.8.6 tool. This research discussed performance matrices and error rates of classifiers for both datasets. The outcomes showed that for the PID database (PIDD), SVM works improved with an accuracy of 74% whereas for Germany RF and KNN work improved with 98.7% accuracy. This study can helps healthcare facilities and researchers in understanding the value and application of ML algorithms in predicting diabetes at an initial stage.\u003Cbr>Keywords: Diabetes mellitus, Logistic regression, Machine learning, Support vector machine, WEKA. |\n| --- | --- |\n\n1. INTRODUCTION  \nNowadays, the world is facing a lot of chronic diseases such as heart disease, cancer, and diabetes. The early finding of these illnesses is critical. The patient must suffer these diseases for a very long time. Various studies are being done to control these diseases. But these diseases are becoming more established day by day. More research is essential to control these diseases. This paper will observe diabetes mellitus (DM), one of the chronic diseases. DM usually known as diabetes, is a metabolic disorder obvious by high blood sugar levels. In this insulin moves sugar from the bloodstream into cells and is accumulated or utilized to form energy. In the condition of diabetes patient's body is not able to produce sufficient insulin or stop producing insulin. Chronic DM poses numerous health concerns and issues for humans. Type-1, type-2, pre-diabetes, and gestational diabetes are the most prevalent variations of DM. Type-1 diabetes is a chronic disorder in which the patient's immune system assaults and abolishes the beta cells in the pancreas that secrete insulin. In type-2 diabetes, the body's insulin secretion diminishes, resulting in high blood sugar levels. According to recent studies, early identification can pre","cbCaiurlvTiEAa7i","https://ap.wps.com/l/cbCaiurlvTiEAa7i","pdf",536170,1,13,"English","en",105,"# Introduction\n## Statistics of diabetes in India\n## Objective","[{\"question\":\"Which machine learning algorithms are compared for diabetic prediction?\",\"answer\":\"Support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT) are compared.\"},{\"question\":\"Which datasets are used to evaluate the classifiers?\",\"answer\":\"The study uses the Pima Indian diabetic (PID) dataset and the Germany diabetes dataset.\"},{\"question\":\"What tool is used for the research implementation and analysis?\",\"answer\":\"The research is implemented and analyzed using the WEKA 3.8.6 environment for knowledge analysis.\"}]","Comparative Analysis of Diabetic Prediction Using Machine Learning Algorithms - Abstract, Introduction and Objectives | PDF",1785895369,33,{"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},"comparative-analysis-of-diabetic-prediction-using-machine-learning-algorithms-abstract-introduction-and-objectives","",{"@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/comparative-analysis-of-diabetic-prediction-using-machine-learning-algorithms-abstract-introduction-and-objectives/124916/",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},"Which machine learning algorithms are compared for diabetic prediction?","Question",{"text":75,"@type":76},"Support vector machine (SVM), Naïve Bayes (NB), K nearest neighbor (KNN), random forest (RF), logistic regression (LR), and decision tree (DT) are compared.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which datasets are used to evaluate the classifiers?",{"text":80,"@type":76},"The study uses the Pima Indian diabetic (PID) dataset and the Germany diabetes dataset.",{"name":82,"@type":73,"acceptedAnswer":83},"What tool is used for the research implementation and analysis?",{"text":84,"@type":76},"The research is implemented and analyzed using the WEKA 3.8.6 environment for knowledge analysis.","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"]