[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118550-en":3,"doc-seo-118550-105":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},118550,13056703019662,"Evangeline","https://ap-avatar.wpscdn.com/avatar/be000253a8e92610077?_k=1778726343310543188",8,"Research & Report","Comparative Analysis of Machine Learning Models for Diabetes Prediction - Research Report","Diabetes is a chronic hyperglycemia-driven disease that creates long-term health and economic burdens worldwide, making timely detection essential for effective intervention. This study conducts a comparative evaluation of four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Linear Regression—using the Pima Indian Diabetes dataset. Models are assessed with accuracy, precision, recall, and F1-score, targeting reliable identification of diabetic individuals. Logistic Regression and Gradient Boosting achieve the highest accuracy (75%), while Random Forest and thresholded Linear Regression show slightly lower performance (72% and 73.16%), supporting ML for clinical decision support.","| Publisher\u003Cbr>\u003Cbr>[www.vandanapublications.com](www.vandanapublications.com) | \u003Cbr>\u003Cbr>\u003Cbr>2025 Volume 15 Number 3 June | E-ISSN:2250-0758\u003Cbr>P-ISSN:2394-6962\u003Cbr> |\n| --- | --- | --- |\n| \u003Cbr>\u003Cbr>1*234\u003Cbr>DOI:10.5281/zenodo.15867611\u003Cbr>1* Pratiksha Patil, Department of Mathematics, Ramsheth Thakur College of Commerce and Science, Kharghar, Maharashtra, India.\u003Cbr>2 Deepali Lawand, Department of Mathematics, Ramsheth Thakur College of Commerce and Science, Kharghar, Maharashtra, India.\u003Cbr>3 Mohit Gambas, Ramsheth Thakur College of Commerce and Science, Kharghar, Maharashtra, India.\u003Cbr>4 Deepak Gaikwad, Department of Physics, KTSP Mandal’s KMC College, Khopoli, Maharashtra, India.\u003Cbr>Diabetes is a chronic health condition affecting millions worldwide, and early detection plays a vital role in effective disease management and prevention. In this study, we conduct a comparative analysis of four machine learning models—Logistic Regression, Random Forest, Gradient Boosting, and Linear Regression—applied to the Pima Indian Diabetes dataset obtained from Kaggle. The dataset comprises diagnostic measurements of female patients aged 21 and above of Pima Indian heritage. Each model is evaluated using key classification metrics, including accuracy, precision, recall, and F1-score. Among the models, Logistic Regression and Gradient Boosting achieved the highest accuracy of 75%, while Random Forest and Linear Regression showed slightly lower performance at 72% and 73.16%, respectively. The study highlights the effectiveness of ensemble methods and traditional classifiers in predicting diabetes outcomes and provides insight into their relative strengths for clinical decision support systems. These results suggest that machine learning can be a valuable tool in aiding early diagnosis and improving patient care strategies.\u003Cbr>Keywords: Diabetes Prediction, Machine Learning, Logistic Regression, Random Forest, Gradient Boosting, Linear Regression, Predictive Analytics. Corresponding Author How to Cite this Article To Browse Pratiksha Patil, Department of Mathematics, Patil P, Lawand D, Gambas M, Gaikwad D, Ramsheth Thakur College of Commerce and Science, Comparative Analysis of Machine Learning Models for Kharghar, Maharashtra, India. Diabetes Prediction. Int J Engg Mgmt Res . Email:\u003Cbr> 2025 ; 15(3):89-93 . Available From [https://ijemr.vandanapublications.com/index.php/j/a](https://ijemr.vandanapublications.com/index.php/j/a) rticle/view/1771\u003Cbr>\u003Cbr>Manuscript Received Review Round 1 Review Round 2 Review Round 3 Accepted\u003Cbr>2025-05-13 2025-06-07 2025-06-21\u003Cbr>Conflict of Interest Funding Ethical Approval Plagiarism X-checker Note\u003Cbr>None Nil Yes 4.32 \u003Cbr>© 2025 by Patil P, Gambas M, Gaikwad D and Published by Vandana Publications. This is an Open Access article licensed under a Creative Commons Attribution 4.0 International License [https://creativecommons.org/licenses/by/4.0/ unported](https://creativecommons.org/licenses/by/4.0/ unported) [CC BY 4.0].\u003Cbr> |  |  |\n| Int J Engg Mgmt Res 2025153 |  | \u003Cbr>89 |\n\n\n| Patil P., et al. Comparative Analysis of Machine Learning |  |  |\n| --- | --- | --- |\n| \u003Cbr>Diabetes mellitus, particularly Type 2 diabetes, isone of the most pervasive and growing public health challenges worldwide. It is characterized by chronic hyperglycemia due to insulin resistance or insufficient insulin production. According to the International Diabetes Federation (2021), approximately 537 million adults were living with diabetes in 2021, a number expected to rise to 783 million by 2045 if left unchecked. The disease not only impacts individual quality of life but also imposes significant economic and healthcare burdens globally. Early diagnosis and intervention are crucial to managing diabetes and mitigating associated complications such as cardiovascular disease, neuropathy, nephropathy, and retinopathy [1] .\u003Cbr>Traditional diagnostic methods like fasting plasma glucose and oral glucose tolerance tests, although effective, m","cbCaivEioRjCYcNp","https://ap.wps.com/l/cbCaivEioRjCYcNp","pdf",511885,1,5,"English","en",105,"# Introduction\n## Machine Learning for Medical Diagnostics\n## Dataset Description\n# Methods\n## Models Compared\n## Evaluation Metrics\n# Results and Discussion\n## Comparative Performance","[{\"question\":\"Which machine learning models are compared for diabetes prediction?\",\"answer\":\"The study compares Logistic Regression, Random Forest, Gradient Boosting, and threshold-adapted Linear Regression.\"},{\"question\":\"What dataset is used in the analysis?\",\"answer\":\"The Pima Indian Diabetes dataset from Kaggle is used, containing records of female patients aged 21 and above with eight clinical variables and a binary diabetes outcome.\"},{\"question\":\"How are model performances evaluated?\",\"answer\":\"Each model is evaluated using accuracy, precision, recall, and F1-score, with emphasis on correctly identifying diabetic individuals.\"}]","Comparative Analysis of Machine Learning Models for Diabetes Prediction - Research Report | PDF",1785684099,13,{"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-machine-learning-models-for-diabetes-prediction-research-report","",{"@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-machine-learning-models-for-diabetes-prediction-research-report/118550/",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},"Which machine learning models are compared for diabetes prediction?","Question",{"text":75,"@type":76},"The study compares Logistic Regression, Random Forest, Gradient Boosting, and threshold-adapted Linear Regression.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset is used in the analysis?",{"text":80,"@type":76},"The Pima Indian Diabetes dataset from Kaggle is used, containing records of female patients aged 21 and above with eight clinical variables and a binary diabetes outcome.",{"name":82,"@type":73,"acceptedAnswer":83},"How are model performances evaluated?",{"text":84,"@type":76},"Each model is evaluated using accuracy, precision, recall, and F1-score, with emphasis on correctly identifying diabetic individuals.","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},[93,97,101,105,109,114,119,122,127,130,134],{"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":21,"doc_module":4,"doc_module_name":46,"category_name":106,"show_sort_weight":107,"slug":108},"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":46,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},9,"Religion & Spirituality",20,"religion-spirituality",{"id":125,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":125,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":21,"slug":137},19,"General","general"]