[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117859-en":3,"doc-seo-117859-105":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},117859,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",7,"Healthcare","Diabetes Diagnosis through Machine Learning - An Analysis of Classification Algorithms","Diabetes is a chronic disease defined by elevated blood sugar and can cause serious complications without timely care. Traditional diagnosis often requires clinic visits, while machine learning can enable earlier and more accurate identification to support prompt intervention. This study builds a predictive model for diabetes patients using Logistic Regression, Decision Tree, and Naive Bayes, trained on the Pima Indians Diabetes Database (PIDD). Model performance is assessed with accuracy, precision, F-measure, and recall, and results indicate Logistic Regression achieves the highest accuracy of 71.39%.","Ahmed et al. LGURJCSIT 2023  \nLGU Research Journal of Computer Science & IT  \nSSN: 2521-0122 (Online)  \nISSN: 2519-7991 (Print)  \ndoi: 10.54692/lgurjcsit.2023.0701411  \nVol. 7 Issue 1, January – March 2023  \nDiabetes Diagnosis through Machine Learning: An Analysis of Classification Algorithms  \nHaris Ahmed1*, Dr.Muhammad AffanAlim1, Dr.Waleej Haider2, Muhammad Nadeem2, Ahsan Masroor1,  \nNadeem Qamar1  \n1College of Computing and Information Sciences, Karachi Institute of Economics and Technology,  \nKarachi, Pakistan.  \n2Department of Computer Science & Information Technology, Sir Syed University of Engineering and  \nTechnology, Karachi, Pakistan  \n[Email: harisahmed19@hotmail.com](Email: harisahmed19@hotmail.com)  \nABSTRACT:  \nDiabetes is a serious and chronic disease characterized by high blood sugar levels. If left untreated, it can lead to numerous complications. In the past, diagnosing diabetes required a visit to adiagnostic center and consultation with a doctor. However, machine learning can help identify the disease earlier and more accurately, providing significant benefits for early intervention and treatment. This study aimed to create a model that can accurately predict the likelihood of diabetes inpatients using three machine learning classification algorithms: Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB). The model was tested on the Pima Indians Diabetes Database (PIDD) from the UCI machine learning repository and the performance of the algorithms was evaluated using various metrics such as accuracy, precision, F-measure, and recall. The results showed that Logistic Regression had the highest accuracy at 71.39%, outperforming the other algorithms, demonstrating the potential of machine learning in enhancing diabetes diagnosis and management.  \nKEYWORDS: Logistic Regression, Naive Bayes, Decision Tree, Machine Learning, Logistic Regression, Diabetes.  \n1. INTRODUCTION  \nDiabetes is a significant health challenge that affects equally developed and underdeveloped countries. It is a chronic, incurable disease characterized by high blood sugar levels due to complications with insulin production [1] . Numerous factors can cause diabetes, including poor diet, toxic substances in food, environmental pollution, infections, unhealthy eating habits, lifestyle changes, and obesity [2] . If not managed properly, diabetes can result in severe consequences. Such as kidney failure, blindness, coma, damage to the pancreas, peripheral vascular diseases, cardiovascular dysfunction, and weight loss [3]. According to estimates, there were 452 million persons with diabetes  \nworldwide in 2017, and this figure is likely to rise to 700 million by 2045. Some research has indicated that the number of people with diabetes could reach halfa billion by 2030 and increase by 25% or 51% by 2045 [4] . While there is no permanent cure for diabetes, early detection and treatment can help to prevent complications. Research and medical professionals agree that the chances of recovery are higher if the disease is detected timely [5] .  \nMachine learning algorithms are beneficial for the timely detection of diseases and analysis of diseases using advanced technology [6] . Machine learning (ML), a subfield of artificial intelligence (AI), encompasses various techniques for discovering patterns in data and achieving  \nLGU Research Journal of Computer Science & Information Technology 7(1) LGURJCSIT 14  \nspecific outcomes [7] . It is classified into four main categories: supervised learning, semi-supervised learning, reinforcement learning, and unsupervised learning.  \nIn order to demonstrate the potential of machine learning in early diagnosis and guiding healthcare professional's decisions in managing diabetes, this study applies and analyses supervised learning approaches, such as Decision Tree, Logistic Regression, and Naive Bayes, for predicting diabetes. In this study, supervised learning techniques such as Decision Tree (DT), Logistic Regression (LR), and Naive ","cbCaiaqGCSSrDCIa","https://ap.wps.com/l/cbCaiaqGCSSrDCIa","pdf",1905579,1,6,"English","en",105,"# Introduction\n# Related Work\n# Research Methodology\n# Results and Discussion\n# Conclusion","[{\"question\":\"What is the main goal of this study on diabetes diagnosis?\",\"answer\":\"To develop and evaluate machine learning models that predict the likelihood of diabetes patients using classification algorithms.\"},{\"question\":\"Which classification algorithms are used in the study?\",\"answer\":\"Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB).\"},{\"question\":\"How is the model performance evaluated, and which algorithm performs best?\",\"answer\":\"Performance is measured using accuracy, precision, F-measure, and recall; Logistic Regression achieves the highest accuracy at 71.39%.\"}]","Diabetes Diagnosis through Machine Learning - An Analysis of Classification Algorithms | PDF",1785680034,15,{"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},"diabetes-diagnosis-through-machine-learning-an-analysis-of-classification-algorithms","",{"@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/diabetes-diagnosis-through-machine-learning-an-analysis-of-classification-algorithms/117859/",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},"What is the main goal of this study on diabetes diagnosis?","Question",{"text":75,"@type":76},"To develop and evaluate machine learning models that predict the likelihood of diabetes patients using classification algorithms.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which classification algorithms are used in the study?",{"text":80,"@type":76},"Logistic Regression (LR), Decision Tree (DT), and Naive Bayes (NB).",{"name":82,"@type":73,"acceptedAnswer":83},"How is the model performance evaluated, and which algorithm performs best?",{"text":84,"@type":76},"Performance is measured using accuracy, precision, F-measure, and recall; Logistic Regression achieves the highest accuracy at 71.39%.","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},[93,97,101,105,110,114,117,122,127,130,134],{"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":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":111,"show_sort_weight":112,"slug":113},"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":115,"slug":116},40,"healthcare",{"id":118,"doc_module":4,"doc_module_name":46,"category_name":119,"show_sort_weight":120,"slug":121},8,"Research & Report",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":106,"slug":137},19,"General","general"]