[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127160-en":3,"doc-seo-127160-105":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},127160,3985741905716,"Rowan","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Prediction of Diabetes Using Machine Learning and Deep Learning Approaches - A Survey","Diabetes is a highly prevalent and severe illness that harms overall health and can lead to major complications including blindness, kidney problems, and heart disease. Early and precise identification is therefore essential to reduce risk and prevent progression. Traditional screening requires patients to visit diagnostic facilities and consult physicians, often resulting in long decision cycles. Recent work using machine learning and deep learning offers more informative outcomes. This survey reviews diabetes prediction approaches based on ML and DL and highlights challenges and future research directions for building optimal detection systems.","Prediction of Diabetes Using Machine Learning and Deep Learning Approaches: A Survey  \nPreetha Rajagopalam.M 1*, Dr.Anuratha.V2 and Dr.Elamparithi.M3  \nDepartment Of Computer Science, Kamalam College of Arts and Science, Anthiyur,  \nBharathiar University, Coimbatore, Tamil Nadu, India.  \nE-Mail:1*[preethamuttikkal@gmail.com](preethamuttikkal@gmail.com), [2](2profanuratha@gmail.com)[profanuratha@gmail.com](2profanuratha@gmail.com), [3](3profelamparithi@gmail.com)[profelamparithi@gmail.com](3profelamparithi@gmail.com)  \nAbstract: One of the most prevalent and severe illnesses in the world today is diabetes. In addition to being bad for the blood, it also leads to several illnesses that kill many people each year, including blindness, kidney problems, and heart problems. Therefore, a system that can precisely identify people with diabetes utilizing their medical information needs to be developed. Numerous traditional methods exist for monitoring the health of people with diabetes. Patients must attend a diagnostic facility, speak with their doctor, and wait for some time to get the findings of the typical screening process. While several techniques have been developed over the last few years to identify diabetes, approaches such as machine learning (ML) and deep learning (DL) provide more informative outcomes. This paper reviewed all diabetes predictions based on ML and DL approaches. Furthermore, to create optimal solutions for diabetes detection and prediction, this study emphasizes the difficulties and potential avenues for future research in this field.  \nKeywords: Diabetes detection, Pima Indians Diabetes, Machine Learning, Deep Learning, fine-tuned algorithm, hybrid model.  \n1. INTRODUCTION  \nIn both developed and emerging nations, diabetes is a disease that is getting worse and more morbid [1]. Diabetes is a longterm medical condition that directly harms the pancreas, thereby making insulin production impossible for the body. Numerous variables, including overweight, sedentary behaviour, high blood pressure, and abnormal cholesterol levels, can contribute to diabetes [2] . Diabetes can raise the chance of dying young and lead to problems in many different areas of the body. Diabetes affects more people than only those who are ill. In addition, the illness has an impact on the ill person’s family as well as the entire community. Diabetes has become a widespread issue. Based on WHO estimates, 422 million people worldwide have diabetes. The majority of these people reside in nations with low and moderate incomes. Diabetes is a cause of death for 1.6 million people annually [3] . The following are the major types of diabetes found in humans: Type 1 DM, Type 2 DM, gestational, and presentational diabetes. Medical diagnosis is one of medical science's most challenging and essential jobs. The patient's plasma glucose quantity, diastolic pressure, triceps folds of skin thickness, blood insulin, body mass, age, and other  \nparameters are measured to forecast diabetes disease. The patient then sees a specialist physician. The decision-making process is entirely drawn out and can occasionally take many months or even weeks, making the physician's work extremely challenging.  \nA vast number of medical datasets are readily available these days. As a result, handling enormous amounts of data by humans might be challenging or even impossible [4] . The majority of diabetic people must live with their condition for the rest of their lives because they usually find out about it too late to receive a complete cure. As a result, efficient computer-based methods are preferred to conventional methods. Technologies based on computers improve accuracy while saving both time and money. The goal is to create a prediction model that can reliably identify diabetes atan early stage and shield people from developing the disease by combining DL and ML algorithms [5] . ML has emerged as a promising approach to diagnose and prevent diabetes. To identify d","cbCaib9I8WQMqNQd","https://ap.wps.com/l/cbCaib9I8WQMqNQd","pdf",277428,1,6,"English","en",105,"# Introduction\n## Diabetes overview and impact\n## Motivation for computer-based prediction\n## ML methods for early detection\n## DL methods for medical prognosis\n# Background Information\n## Diabetes prediction pipeline and key stages\n## Dataset collection","[{\"question\":\"Why is early diabetes prediction important?\",\"answer\":\"Early prediction helps identify people with diabetes sooner, supporting prevention of disease progression. It also reduces delays associated with traditional diagnosis workflows.\"},{\"question\":\"What data sources are commonly used for diabetes prediction models?\",\"answer\":\"The survey mentions the National Institute of Diabetes Pima Indians Diabetes (PID) database and Kaggle as widely used datasets for training and evaluation.\"},{\"question\":\"How do machine learning and deep learning approaches differ in diabetes detection?\",\"answer\":\"Machine learning methods like SVM, KNN, Random Forest, Logistic Regression, and Naïve Bayes are used for classification but may struggle with large datasets and require feature learning. Deep learning can handle large volumes of data and is reported to achieve higher classification accuracy while being more resilient to noise.\"}]","Prediction of Diabetes Using Machine Learning and Deep Learning Approaches - A Survey | PDF",1785937262,15,{"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},"prediction-of-diabetes-using-machine-learning-and-deep-learning-approaches-a-survey","",{"@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-and-deep-learning-approaches-a-survey/127160/",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},"Why is early diabetes prediction important?","Question",{"text":75,"@type":76},"Early prediction helps identify people with diabetes sooner, supporting prevention of disease progression. It also reduces delays associated with traditional diagnosis workflows.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data sources are commonly used for diabetes prediction models?",{"text":80,"@type":76},"The survey mentions the National Institute of Diabetes Pima Indians Diabetes (PID) database and Kaggle as widely used datasets for training and evaluation.",{"name":82,"@type":73,"acceptedAnswer":83},"How do machine learning and deep learning approaches differ in diabetes detection?",{"text":84,"@type":76},"Machine learning methods like SVM, KNN, Random Forest, Logistic Regression, and Naïve Bayes are used for classification but may struggle with large datasets and require feature learning. Deep learning can handle large volumes of data and is reported to achieve higher classification accuracy while being more resilient to noise.","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},[93,97,101,105,110,114,119,122,127,130,134],{"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":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":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":106,"slug":137},19,"General","general"]