[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121365-en":3,"doc-seo-121365-105":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},121365,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","A Study on Data Analysis and Prediction of Diabetes Using Machine Learning Models - Research Report","A Study on Data Analysis and Prediction of Diabetes Using Machine Learning Models explores diabetes prediction through data mining and machine learning. The work reviews methods supporting prediction and diagnosis, diabetic complications, genetic background and environment, and health care management, highlighting clinical datasets and supervised learning dominance. It evaluates models for analyzing diabetes-related attributes such as age, gender, hypertension, smoking history, BMI, HbA1c, and blood glucose, using algorithms including linear regression and tree-based methods with test statistics or accuracy measures. The study emphasizes that predictions should support, not replace, clinical diagnosis.","A Study on Data Analysis and Prediction of Diabetes Using Machine Learning Models  \nDr. N. Ravi1, Dr. P. Rajesh2  \n1Assistant Professor, PG Department of Computer Science, Government Arts College, Chidambaram – 608 102, Tamil Nadu, India.  \n2Assistant Professor, PG Department of Computer Science, Government Arts College, Chidambaram – 608 102,(Deputed from Dept. of Computer and Information Science, Annamalai University, Annamalainagar-608 002) Tamil Nadu, India.  \n(Received: 02 September 2023 Revised: 14 October Accepted: 07 November)  \nKEYWORDS  \nMachine learning, diabetes prediction,  \ndecision tree,  \ncorrelation coefficient, and test statistics.  \nABSTRACT:  \nIt's crucial to emphasize that although these approaches may aid in diabetes prediction, it is essential for a healthcare provider to make the diagnosis and offer guidance on managing the condition. While AI and machine learning-based diabetes prediction models are advancing in accuracy and sophistication, they should complement medical expertise as supportive tools. Data mining is typically described as the practice of employing computer systems and automation to explore extensive datasets, identifying patterns and trends, and converting these discoveries into valuable business insights and predictive analyses. This paper considers diabetes prediction-related dataset data like gender, age, hypertension, heart disease, smoking history, bmi, HbA1c level, Blood Glucose level, diabetes. The machine learning approaches which is used to analysis and predict the dataset using linear regression, decision stump, M5P, random forest, random tree, and REP tree. Numerical illustrations are provided to prove the proposed results with test statistics or accuracy parameters.  \n1. Introduction and Literature Review  \nThe realm of diabetes prediction research, leveraging data mining and machine learning, stands asa compelling and essential area of investigation. This field employs advanced computational methods to scrutinize vast datasets encompassing factors associated with diabetes risk, patient characteristics, and health metrics.  \nMachine learning engineers craft autonomous systems through programming. These algorithms automatically gather data and leverage it to acquire knowledge. These systems are anticipated to discern data patterns and autonomously make significant decisions. Data mining is employed for delving into ever-expanding databases and enhancing market segmentation. By scrutinizing the connections between variables like customer age, gender, preferences, and more, it becomes feasible to predict their behaviors and tailor personalized loyalty campaigns accordingly.  \nThe objective of this current study is to systematically review the utilization of machine learning, data mining techniques, and tools in the domain  \nof diabetes research, focusing on a) Prediction and Diagnosis, b) Diabetic Complications, c) Genetic Background and Environment, and d) Health Care and Management. Notably, the primary category appears tobe Prediction and Diagnosis. A diverse range of machine learning algorithms was employed, with approximately 85% utilizing supervised learning approaches and 15% employing unsupervised methods, specifically association rules. Notably, Support Vector Machines (SVM) emerged as the most prominent and successful algorithm. Clinical datasets were the predominant data type utilized in the selected articles, highlighting the potential for extracting valuable insights to advance our understanding and investigation of Diabetes Mellitus [1] .  \nAccurate prediction is crucial for diagnosing Diabetes, and data mining plays a pivotal role in extracting meaningful information from vast datasets. The primary aim of data mining is to uncover novel patterns and offer valuable insights to aid in medical diagnosis and treatment. This project seeks to mine Diabetes data to enhance classification efficiency by exploring various data mining methods and techniques,  \nultimately i","cbCaip32kc6yRiuQ","https://ap.wps.com/l/cbCaip32kc6yRiuQ","pdf",360495,1,9,"English","en",105,"# Introduction and Literature Review\n## Data mining and machine learning in diabetes research\n## Study objective and research focus areas\n## Algorithms and dataset characteristics","[{\"question\":\"What is the main focus of the diabetes study?\",\"answer\":\"The study focuses on reviewing and evaluating machine learning and data mining techniques for diabetes research, especially prediction and diagnosis, diabetic complications, genetic background and environment, and health care management.\"},{\"question\":\"Which diabetes-related factors does the paper consider in the dataset?\",\"answer\":\"The paper considers patient and clinical attributes such as gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose level, along with diabetes presence.\"},{\"question\":\"How are the machine learning models evaluated in the study?\",\"answer\":\"The paper analyzes and predicts using multiple machine learning methods, and provides numerical illustrations using test statistics and/or accuracy parameters to support the proposed results.\"}]","A Study on Data Analysis and Prediction of Diabetes Using Machine Learning Models - Research Report | PDF",1785735259,23,{"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-study-on-data-analysis-and-prediction-of-diabetes-using-machine-learning-models-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/a-study-on-data-analysis-and-prediction-of-diabetes-using-machine-learning-models-research-report/121365/",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","2026-08-03",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 focus of the diabetes study?","Question",{"text":75,"@type":76},"The study focuses on reviewing and evaluating machine learning and data mining techniques for diabetes research, especially prediction and diagnosis, diabetic complications, genetic background and environment, and health care management.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which diabetes-related factors does the paper consider in the dataset?",{"text":80,"@type":76},"The paper considers patient and clinical attributes such as gender, age, hypertension, heart disease, smoking history, BMI, HbA1c level, and blood glucose level, along with diabetes presence.",{"name":82,"@type":73,"acceptedAnswer":83},"How are the machine learning models evaluated in the study?",{"text":84,"@type":76},"The paper analyzes and predicts using multiple machine learning methods, and provides numerical illustrations using test statistics and/or accuracy parameters to support the proposed results.","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},[93,97,101,105,110,115,120,123,127,130,134],{"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":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":21,"doc_module":4,"doc_module_name":46,"category_name":124,"show_sort_weight":125,"slug":126},"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"]