[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123282-en":3,"doc-seo-123282-105":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},123282,687197207057,"Sage","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Prediction of Diabetes Using Machine Learning - Thesis","This research compares the overall health of diabetic patients with healthy counterparts using a large dataset covering pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age. Higher average glucose, higher BMI, and older age characterize diabetic participants relative to the healthy group. Obesity emerges as a significant diabetes risk factor, with many patients showing BMI above healthy ranges, and insulin resistance is linked to diabetes prevalence. Results support the value of family history and age and highlight screening for at-risk individuals, achieving about 82% diagnostic accuracy.","Rochester Institute of Technology  \nRIT Digital Institutional Repository  \nTheses  \n4-28-2025  \nPrediction of Diabetes Using Machine Learning  \nWalid Abdulla Abdulrahim [wa5147@rit.edu](wa5147@rit.edu)  \nFollow this and additional works at: [https://repository.rit.edu/theses](https://repository.rit.edu/theses)  \nRecommended Citation  \nAbdulrahim, Walid Abdulla, \"Prediction of Diabetes Using Machine Learning\" (2025) . Thesis. Rochester Institute of Technology. Accessed from  \nThis Thesis is brought to you for free and open access by the RIT Libraries. For more information, please contact [repository@rit.edu](repository@rit.edu).  \nPrediction of Diabetes Using Machine  \nLearning  \nby  \nWalid Abdulla Abdulrahim  \nA Thesis Submitted in Partial Fulfilment of the Requirements for the Degree of Master of Science in Professional Studies:  \nData Analytics  \nDepartment of Graduate Programs & Research  \nRochester Institute of Technology  \nRIT Dubai  \n28 Apr 2025  \nRIT  \nMaster of Science in Professional Studies: Data Analytics  \nGraduate Thesis Approval  \nStudent Name: Walid Abdulla Abdulrahim  \nThesis Title: Prediction of Diabetes Using Machine Learning  \nGraduate Committee:  \nName: Dr. Sanjay Modak Date:  \nChair of committee  \nName: Ayman Ibrahim Date:  \nMember of committee  \nABSTRACT  \nThis research seeks to compare the overall health of diabetic patients with that of their healthy counterparts based on various heath indicators obtained from a large dataset comprising of pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function and age. Relative to the healthy group, increased average glucose level, higher BMI, and older age are identified in the diabetic participants. Most importantly, the results show that obesity is a significant risk factor for diabetes, and many diabetics have BMIs that are elevated above healthy levels. The data also points towards the fact that insulin resistance and prevalence of diabetes are related to each other, as exemplified by the range of insulin variation in type 2 diabetes subjects.  \nThus, the study also underscores the significance of family history and age in the development of diabetes, underlining the need for people with diabetes risk factors to undergo screening as soon as possible. The findings revealed an accuracy of close to 82% in diagnosing the condition from the dataset whose algorithms inform machine learning models that are already applied in clinical practice. These findings attest to the complex nature of the diseases and emphasize the  \nimportance of integrating various concepts and thinking in relation to diabetes risk factors to help ensure early detection and successful management of the conditions. In conclusion, this research enhances the literature on the application of health data analytics in enhancement of diabetes prevention and management.  \nKeywords: Diabetic, BMI, Type-2 Diabetes. Insulin variation, diabetic risk-factors, diabetes prevention  \nTable of Contents  \nContents  \nABSTRACT.................................................................................................................................... 3  \nTable of Contents ............................................................................................................................ 5  \nTable of Figures .............................................................................................................................. 7  \nTable of Tables ................................................................................................................................ 8  \nAcknowledgements ......................................................................................................................... 9  \n1. INTRODUCTION ................................................................................................................. 10  \n1.1 Background ..........................................................................................","cbCaid07fBXfY0Mj","https://ap.wps.com/l/cbCaid07fBXfY0Mj","pdf",2093472,1,64,"English","en",105,"# ABSTRACT\n# Table of Contents\n## Table of Figures\n## Table of Tables\n## Acknowledgements\n# 1. INTRODUCTION\n## 1.1 Background\n## 1.2 Problem Statement\n## 1.3 Research Aim and Objectives\n## 1.4 Significance of Study\n## 1.5 Thesis Definition and Goals\n# 2. LITERATURE REVIEW\n## 2.1 Overview of Diabetes Prediction and Current Approaches\n## 2.2 Machine Learning in Healthcare\n## 2.3 Comparative Studies of Machine Learning Algorithms for Prediction\n## 2.4 Feature Importance in Diabetes Prediction\n## 2.5 Research Gaps\n# 3. METHODOLOGY\n## 3.1 Dataset Description\n## 3.2 Data Preprocessing\n## 3.3 Data Acquisition\n## 3.4 Feature Analysis and Selection\n## 3.5 Model Selection and Training","[{\"question\":\"Which health indicators are used to predict diabetes in this study?\",\"answer\":\"The study uses indicators including pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age.\"},{\"question\":\"What factors show significant differences between diabetic and healthy participants?\",\"answer\":\"Diabetic participants show increased average glucose levels, higher BMI, and older age compared with the healthy group.\"},{\"question\":\"How accurate are the models for diagnosing diabetes based on the dataset?\",\"answer\":\"The findings report accuracy close to 82% for diagnosing diabetes using the algorithms informed by the dataset.\"}]","Prediction of Diabetes Using Machine Learning - Thesis | PDF",1785815729,161,{"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},"prediction-of-diabetes-using-machine-learning-thesis","",{"@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/prediction-of-diabetes-using-machine-learning-thesis/123282/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which health indicators are used to predict diabetes in this study?","Question",{"text":75,"@type":76},"The study uses indicators including pregnancies, glucose levels, blood pressure, skin thickness, insulin, BMI, diabetes pedigree function, and age.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What factors show significant differences between diabetic and healthy participants?",{"text":80,"@type":76},"Diabetic participants show increased average glucose levels, higher BMI, and older age compared with the healthy group.",{"name":82,"@type":73,"acceptedAnswer":83},"How accurate are the models for diagnosing diabetes based on the dataset?",{"text":84,"@type":76},"The findings report accuracy close to 82% for diagnosing diabetes using the algorithms informed by the dataset.","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"]