[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122356-en":3,"doc-seo-122356-105":30,"detail-sidebar-cat-0-en-105":92},{"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":20,"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},122356,1374391974468,"Eden","https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0",8,"Research & Report","Early Detection of Diabetes Mellitus in Women via Machine Learning - research report","Diabetes Mellitus (DM) is a major global health threat, driving 6.7 million deaths in 2021 and remaining a leading cause of mortality in Indonesia. This study builds a machine-learning prediction model for diabetes in women with a target accuracy of at least 85%, and integrates a chatbot-based interface for early screening. The research uses primary women-focused data plus the UCI Pima Indians Diabetes Database, and evaluates Decision Tree, Logistic Regression, and Random Forest using accuracy, precision, recall, and F1-score.","Early Detection of Diabetes Mellitus in Women via Machine  \nLearning  \nAhmad Zaki Arrayyan, Sisdarmanto Adinandra*  \nMaster Program of Electrical Engineering, Faculty of Industrial Technology, Universitas Islam Indonesia  \n*Corresponding author, e-mail: [s.adinandra@uii.ac.id](s.adinandra@uii.ac.id)  \nAbstract – Diabetes Mellitus (DM) is a major global health concern, responsible for 6.7 million deaths in 2021, equivalent to one death every five seconds. In Indonesia, it was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people. This study focuses on developing a diabetes prediction model using machine learning, aiming for an accuracy of at least 85%, and incorporates a chatbot-based system to identify potential diabetes in women. The research utilizes primary data, including glucose levels, blood pressure, body mass index, and age, as well as secondary data, such as pregnancy-related metrics, from the UCI Pima Indians Diabetes Database, which contains 768 records with eight attributes. The study evaluates the performance of three machine learning algorithms: Decision Tree, Logistic Regression, and Random Forest, using metrics such as accuracy, precision, recall, and F1-score. Among these models, the Decision Tree demonstrates excellent performance for Class 0, with precision, recall, and F1-score all at 0.97. However, its performance for Class 1, while decent, leaves room for improvement, achieving a precision of 0.80 and a recall of 0.84, resulting in an F1-score of 0.82. Logistic Regression also performs well for Class 0, with a precision of 0.95 and a recall of 0.99, yielding an F1-score of 0.97. Yet, it struggles with Class 1, where its precision is high at 0.93, but its recall drops significantly to 0.68, producing an F1-score of 0.79. Lastly, Random Forest emerges as the bestperforming model overall, achieving an accuracy of 0.96. It excels for Class 0, with a precision of 0.96 and a recall of 0.99, leading to an F1-score of 0.97. For Class 1, it maintains high precision (0.93) but exhibits moderate recall (0. 74), resulting in an F1-score of 0.82.  \nKeywords: early detection; diabetes mellitus; public health; machine learning; prediction  \nI. Introduction  \nDiabetes Mellitus (DM) is a significant global health concern, with a marked increase in incidence and mortality. The International Diabetes Federation reported that in 2021, diabetes was responsible for 6.7 million deaths worldwide, equating to one death every five seconds[1], [2]. In Indonesia, diabetes was the third leading cause of death in 2019, with a mortality rate of approximately 57.42 per 100,000 people[3] . DM is classified into two types: Type 1, which is autoimmune in nature, and Type 2, primarily linked to unhealthy lifestyles[4][5] .  \nThe rising number of diabetes cases is exacerbated by insufficient public awareness and understanding of early detection[6]. According to the Riskesdas 2018 survey, the prevalence of diabetes  \namong women, diagnosed by a doctor, was 1.78%, with a confidence interval of 1.73 - 1.84[7] . The detection and management of diabetes remain challenging [8], particularly due to the inefficiency and potential errors associated with manual data processing, as well as the high costs of diagnosis and treatment.  \nTo address these challenges, there is a need for predictive models to facilitate early screening and detection of diabetes. This study aims to develop a machine learning-based prediction model for diabetes, targeting an accuracy of at least 85% . Additionally, the research seeks to implement a chatbot-based prediction tool specifically for identifying diabetes risk in women. Utilizing machine learning techniques, including Decision Tree, Logistic Regression, and Random Forest algorithms, this study processes both primary and  \nsecondary data from the UCI Pima Indians Diabetes Database. The goal is to evaluate and compare the performance of these algorithms to develop an e","cbCaibmaGuk5G8PV","https://ap.wps.com/l/cbCaibmaGuk5G8PV","pdf",294037,1,7,"English","en",105,"# Introduction\n# Research Method\n## Dataset\n## Preprocessing\n## Learning Model","[{\"question\":\"What is the study’s main goal for diabetes early detection in women?\",\"answer\":\"To develop a machine-learning prediction model that targets at least 85% accuracy and to provide a chatbot-based tool to identify diabetes risk in women.\"},{\"question\":\"Which data sources and features are used in the model?\",\"answer\":\"The study uses primary data from women aged 21–80 and secondary data from the UCI Pima Indians Diabetes Database, including features such as glucose, blood pressure, BMI, age, and related pregnancy and clinical attributes.\"},{\"question\":\"How are the machine learning models evaluated and which performs best overall?\",\"answer\":\"Models are evaluated using accuracy, precision, recall, and F1-score. Random Forest achieves the best overall performance with an accuracy of 0.96.\"}]","Early Detection of Diabetes Mellitus in Women via Machine Learning - research report | PDF",1785810215,18,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"early-detection-of-diabetes-mellitus-in-women-via-machine-learning-research-report","",{"@graph":36,"@context":86},[37,54,69],{"@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/early-detection-of-diabetes-mellitus-in-women-via-machine-learning-research-report/122356/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-05","2026-08-04",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What is the study’s main goal for diabetes early detection in women?","Question",{"text":76,"@type":77},"To develop a machine-learning prediction model that targets at least 85% accuracy and to provide a chatbot-based tool to identify diabetes risk in women.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"Which data sources and features are used in the model?",{"text":81,"@type":77},"The study uses primary data from women aged 21–80 and secondary data from the UCI Pima Indians Diabetes Database, including features such as glucose, blood pressure, BMI, age, and related pregnancy and clinical attributes.",{"name":83,"@type":74,"acceptedAnswer":84},"How are the machine learning models evaluated and which performs best overall?",{"text":85,"@type":77},"Models are evaluated using accuracy, precision, recall, and F1-score. 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