[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123839-en":3,"doc-seo-123839-105":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},123839,4398048949847,"Eliana","https://ap-avatar.wpscdn.com/avatar/400002536579ef2da7f?_k=1778318612642679267",8,"Research & Report","A Survey on Using Machine Learning to Predict Diabetes Early on","Diabetes is a chronic metabolic disorder driven by persistently high blood glucose, and timely early prediction can substantially reduce risk factors and disease severity. Data mining combined with machine learning, a branch of artificial intelligence, supports extracting actionable information from widely available healthcare data to improve prognosis, diagnosis, therapy, medication development, and overall care. Guided by global reports on diabetes growth, the study reviews major early detection techniques, and demonstrates a Random Forest experiment on a diabetes dataset.","A Survey on Using Machine Learning to Predict  \nDiabetes Early on  \nSatyendra Singh Rawat1, Amit Kumar Mishra2, Deepak Motwani3  \n1Department of Computer Science & Engineering, Swami Rama Himalayan University,  \nSwami Ram Nagar, Jollygrant, Dehradun, INDIA  \n2,3Department of Computer Science & Engineering, Amity University,  \nBhind Road, Deen Dayal Nagar, Gwalior, INDIA  \nAbstract-Diabetes is a category of metabolic disease caused by a prolonged high blood sugar level. It is sometimes referred to as a chronic disease. If accurate early prediction is achievable, it can considerably lower the risk factor and severity of diabetes. Combining data mining methods with machine learning, a subsection of artificial intelligence, offers promise in the field of prediction. Data is widely available in the healthcare industry, and in order to improve prognosis, diagnosis, therapy, medication development, and healthcare in general, information must be extracted from it. Based on the World Health Organisation's 2014 report, diabetes is a type of chronic disease with the fastest global growth rates. To illustrate the widely used techniques for early diabetes detection—which are based on cutting-edge technologies including machine learning, cloud computing, etc.—we have reviewed a few significant pieces of literature in this study. The findings suggested that artificial intelligence-based methods are more effective in the early detection of diabetes in patients. Here, we used the Random Forest model to conduct an experiment using a diabetes dataset. First, the dataset is resampled and then used to train and test the Random Forest model. On all performance criteria, the Random Forest attained values above 96% .  \nKeywords: AI, Diabetes, Deep Learning, IoT, Machine Learning,  \n1. INTRODUCTION  \nIt is commonly recognized that excess weight and abdominal fat accumulation pose significant risks for those with type 2 diabetes. Using physical examinations together to foresee the presence of type 2 diabetes is still debatable. The purpose of the research is to forecast, in adult Koreans, the fasting plasma glucose (FPG) level utilised in the determination of type 2 diabetes using a variety of measurements (Lee & Kim, 2016) . Type 2 diabetes is closely linked to the hypertriglyceridemic waist (HW) phenotype (Lee & Kim, 2016). The diagnosis of diabetic retinopathy depends heavily on the detection of microaneurysms (MAs), which are thought to be the initial lesions in the condition (Zhou et al., 2017) . When making medical decisions, estimating the risk of longterm diabetic problems is crucial. The supervision of type 2 diabetes mellitus (T2DM) guidelines includes estimating the risk of cardiovascular disease (CVD) in order to start the right medication (Zarkogianni et al., 2018) . New advances in wearable computing and artificial intelligence, combined with advancements in big data and wireless networking technologies like 5G networks, remedial big data analytics, and the Internet of Things (IoT), are making it possible to create and deploy creative diabetes monitoring apps and systems. It is essential to develop efficient techniques for the diagnosis and treatment of diabetics due to the chronic and  \nsystemic harm that these individuals experience (Chen et al., 2018) . Hyperglycemia is a chronic disorder allied with diabetes mellitus. It can encourage a number of issues. Based on the increasing morbidity in recent times, 642 million people worldwide will have diabetes by 2040, which implies that one in ten adults will have the disease. Without a doubt, careful consideration is needed for this concerning number (Zou et al., 2018) . Continuous glucose monitoring systems (CGMSs), which enable high sample rates for measuring a diabetic patient's blood glucose value, produce a significant amount of data. Machine learning approaches have the potential to utilise this data to infer future glycaemic concentration values, which could lead to better treatment o","cbCaiq8wRiZZlZQA","https://ap.wps.com/l/cbCaiq8wRiZZlZQA","pdf",282550,1,5,"English","en",105,"# Abstract\n# Introduction\n## Diabetes risk, diagnosis, and monitoring\n## Machine learning methods and data sources\n## Experimental approach with Random Forest","[{\"question\":\"Why is early prediction of diabetes important?\",\"answer\":\"Early prediction can reduce risk factors and lessen the severity of diabetes, enabling earlier intervention and better outcomes.\"},{\"question\":\"How does the study position machine learning in diabetes prediction?\",\"answer\":\"It describes machine learning as a promising way to extract patterns from healthcare data, improving diagnosis, prognosis, treatment planning, and monitoring.\"},{\"question\":\"What model and dataset approach does the study use for its experiment?\",\"answer\":\"It applies the Random Forest model, resampling the diabetes dataset and then training and testing the model to evaluate performance.\"}]","A Survey on Using Machine Learning to Predict Diabetes Early on | PDF",1785818824,13,{"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},"a-survey-on-using-machine-learning-to-predict-diabetes-early-on","",{"@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/a-survey-on-using-machine-learning-to-predict-diabetes-early-on/123839/",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},"Why is early prediction of diabetes important?","Question",{"text":75,"@type":76},"Early prediction can reduce risk factors and lessen the severity of diabetes, enabling earlier intervention and better outcomes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the study position machine learning in diabetes prediction?",{"text":80,"@type":76},"It describes machine learning as a promising way to extract patterns from healthcare data, improving diagnosis, prognosis, treatment planning, and monitoring.",{"name":82,"@type":73,"acceptedAnswer":83},"What model and dataset approach does the study use for its experiment?",{"text":84,"@type":76},"It applies the Random Forest model, resampling the diabetes dataset and then training and testing the model to evaluate performance.","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"]