[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-124867-en":3,"doc-seo-124867-105":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},124867,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Enhancing Diabetes Prediction Accuracy through Hybrid Machine Learning Models - A Comparative Study","This study investigates the effectiveness of various machine learning models for predicting diabetes onset, emphasizing that hybrid models outperform single-learner approaches. Using a dataset of 10,000 individuals with features such as glucose level, BMI, insulin, and others, the work performs data preprocessing and feature engineering to optimize inputs for ML. Decision Trees, Random Forest, KNN, and XGBoost are built, then improved through ensemble strategies including stacking and soft voting, achieving high accuracy (98.11%), precision (97.31%), and ROC AUC (99.82%).","Enhancing Diabetes Prediction Accuracy through Hybrid Machine Learning Models: A Comparative Study  \nGregorius Airlangga1􀀍  \n1 Information System Study Program, Engineering Faculty, Atma Jaya Catholic University of Indonesia, Indonesia  \n\n| Informasi Artikel\u003Cbr>Riwayat Artikel\u003Cbr>Received : April 14, 2024\u003Cbr>Revised : April 22, 2024\u003Cbr>Accepted : April 25, 2024\u003Cbr>Keywords:\u003Cbr>Hybrid Model, Decision\u003Cbr>Trees, Random Forest,  XGBoost, Diabetes \u003Cbr>Kata Kunci:\u003Cbr>Hybrid Model, Decision Trees, Random Forest, XGBoost, Diabetes | ABSTRACK |\n| --- | --- |\n|  | This study investigates the effectiveness of various machine learning (ML) models in predicting the onset of diabetes, emphasizing the superior performance of hybrid models over single learner models. Employing a dataset comprising 10,000 individuals with features like Glucose level, BMI, Insulin, and more, we meticulously processed and engineered the data to optimize itfor ML applications. We developed several models, including Decision Trees, Random Forest, KNN, and XGBoost, and then advanced to hybrid models using ensemble techniques like stacking and soft voting classifiers. Ourfindings indicate that hybrid models significantly outperform single learner models. These models achieved remarkable accuracy (98.11%), precision (97.31%), and ROCAUC (99.82%), highlighting their potential in clinical settings. The study underscores the value of hybrid ML models in enhancing predictive accuracy and reliability in diabetes diagnostics. |\n|  | ABSTRAK |\n|  | Penelitian ini menginvestigasi efektivitas berbagai model pembelajaran mesin (ML) dalam memprediksi timbulnya diabetes, dengan menekankan pada kinerja superior model hibrida dibandingkan model pembelajaran tunggal. Menggunakan dataset yang terdiri dari 10.000 individu dengan fitur seperti kadar Glukosa, BMI, Insulin, dan lainnya, kami secara teliti memproses dan mengolah data untuk mengoptimalkannya untuk aplikasi ML. Kami mengembangkan beberapa model, termasuk Decision Trees, Random Forest, KNN, dan XGBoost, kemudian beralih ke model hibrida menggunakan teknik ensemble seperti classifier stacking dan voting lembut. Temuan kami menunjukkan bahwa model hibrida secara signifikan mengungguli model pembelajaran tunggal. Model ini mencapai akurasi (98,11%), presisi (97,31%), dan ROC AUC (99,82%), menyoroti potensi mereka dalam pengaturan klinis. Studi ini menekankan perlunya model ML hibrida dalam meningkatkanakurasi dan keandalan prediktif untuk diagnostik diabetes. |\n| Corresponding Author :\u003Cbr>Gregorius Airlangga\u003Cbr>Information System Study Program, Engineering Faculty, Atma Jaya Catholic University of Indonesia, Indonesia\u003Cbr>Jalan Jenderal Sudirman No. 51 Jakarta Selatan, DKI Jakarta [E-mail:](E-mail: gregorius.airlangga@atmajaya.ac.id)[ ](E-mail: gregorius.airlangga@atmajaya.ac.id)[gregorius.airlangga@atmajaya.ac.id](E-mail: gregorius.airlangga@atmajaya.ac.id) |  |\n\nINTRODUCTION  \nDiabetes mellitus, a group of metabolic diseases characterized by high blood sugar levels over a prolonged period, remains a formidable challenge in the public health domain (Begum et al. , 2023; Chen et al. , 2024; Li et al. , 2024) . The burden of diabetes is extensive, affecting over 422 million people worldwide as of 2014, and is expected to rise to 629 million by 2045 (Bobga Billa, 2023; Olatidoye, 2023; Zawudie et al. , 2022) . This condition not only diminishes the quality of life but also imposes substantial economic burdens on healthcare systems (Alinia et al. , 2021; Kemmaket al. , 2020; Saketkoo et al. , 2021) . As a chronic disease, diabetes requires continuous medical care and patient self-management education to prevent acute complications and to reduce the risk of long-term complications, including neuropathy, nephropathy, and retinal damage (Kropp et al. , 2023; Lodhi, 2021; Misher et al. , 2021) . Given these challenges, there is a critical need for more sophisticated, predictive strategies that can aid in early detection and tailored t","cbCaioWl452BkkIa","https://ap.wps.com/l/cbCaioWl452BkkIa","pdf",332145,1,10,"English","en",105,"# Introduction\n## Background and public health challenge\n## Evolution of diabetes prediction methods\n## Machine learning and ensemble approaches","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"To evaluate how different machine learning models predict diabetes onset and to show whether hybrid models provide better predictive performance than single-learner models.\"},{\"question\":\"What dataset and features are used?\",\"answer\":\"The study uses a dataset of 10,000 individuals with features including glucose level, BMI, insulin, and additional variables relevant to diabetes prediction.\"},{\"question\":\"How are hybrid models constructed in this research?\",\"answer\":\"Hybrid models are formed using ensemble techniques such as stacking and soft voting classifiers, built on top of base models like Decision Trees, Random Forest, KNN, and XGBoost.\"}]","Enhancing Diabetes Prediction Accuracy through Hybrid Machine Learning Models - A Comparative Study | PDF",1785895122,25,{"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},"enhancing-diabetes-prediction-accuracy-through-hybrid-machine-learning-models-a-comparative-study","",{"@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/enhancing-diabetes-prediction-accuracy-through-hybrid-machine-learning-models-a-comparative-study/124867/",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},"What is the main goal of the study?","Question",{"text":75,"@type":76},"To evaluate how different machine learning models predict diabetes onset and to show whether hybrid models provide better predictive performance than single-learner models.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset and features are used?",{"text":80,"@type":76},"The study uses a dataset of 10,000 individuals with features including glucose level, BMI, insulin, and additional variables relevant to diabetes prediction.",{"name":82,"@type":73,"acceptedAnswer":83},"How are hybrid models constructed in this research?",{"text":84,"@type":76},"Hybrid models are formed using ensemble techniques such as stacking and soft voting classifiers, built on top of base models like Decision Trees, Random Forest, KNN, and XGBoost.","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},[93,97,101,105,110,115,120,123,128,131,134],{"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":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":21,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":21,"slug":133},"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":106,"slug":137},19,"General","general"]