[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119981-en":3,"doc-seo-119981-105":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":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},119981,549758252649,"Ivy","https://ap-avatar.wpscdn.com/avatar/8000253669c5317157?_k=1778319167496531819",8,"Research & Report","Comparative Analysis of Machine Learning Models for Predicting Diabetes - Unveiling the Superiority of Advanced Ensemble Methods","The study conducts a comparative analysis of multiple machine learning approaches for diabetes prediction, focusing on advanced ensemble techniques versus traditional algorithms and simpler deep neural network models. It assesses Extra Trees Classifier and LightGBM alongside logistic and classic feature-based predictors using a diabetes-related dataset with variables such as glucose concentration, BMI, and insulin levels. Reliability is strengthened through polynomial feature transformation and tenfold cross-validation, demonstrating near-perfect ROC AUC, accuracy, precision, and F1 scores for the leading ensemble methods.","Comparative Analysis of Machine Learning Models for Predicting Diabetes: Unveiling the Superiority of Advanced Ensemble Methods  \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 15 , 2024\u003Cbr>Revised : April 21, 2024\u003Cbr>Accepted : April 23, 2024\u003Cbr>Keywords:\u003Cbr>Extra Trees; LightGBM; DNN; Diabetes; Ensemble  Learning \u003Cbr>Kata Kunci:\u003Cbr>Extra Trees; LightGBM; DNN; Diabetes; Ensemble Learning | ABSTRACK |\n| --- | --- |\n|  | This study presents a comprehensive analysis of various machine learning models to predict diabetes. The research evaluates and compares the predictive performance of advanced ensemble techniques Extra Trees Classifier and LightGBM with traditional machine learning algorithms and simpler deep neural network (DNN) architectures. The dataset comprises numerous features pertinent to diabetes diagnosis, such as glucose concentration, BMI, and insulin levels, among others. A methodology, including polynomial feature transformation and tenold cross-validation, was employed to ensure the study's reliability and the models' capability to generalize. The advanced ensemble models, Extra Trees and LightGBM, achieved stellar predictive metrics, with the former attaining a near-perfect ROC AUC, accuracy, precision, and an F1 score close to 1. LightGBM followed closely, demonstrating the high efficacy of ensemble methods in complex data settings. These results were contrasted with significantly lower performance metrics from DNNs and respectable, albeit lower, scores from traditional models like Decision Trees, Random Forest, KNN, and XGBoost. |\n|  | ABSTRAK |\n|  | Penelitian ini menyajikan analisis komprehensif dari berbagai model pembelajaran mesin untuk memprediksi penyakit diabetes. Penelitian ini mengevaluasi dan membandingkan kinerja prediktif dari teknik ensemble seperti Extra Trees Classifier dan LightGBM denganalgoritma pembelajaran mesin tradisional dan arsitektur jaringan saraftiruan (DNN) yang sederhana. Dataset mencakup berbagai fitur yang relevan untuk diagnosis diabetes, seperti konsentrasi glukosa, BMI, dan tingkat insulin, di antara lainnya. Metodologi yang digunakan, termasuk transformasi fitur polinomial dan validasi silang sepuluh kali lipat, yangmana digunakan untuk memastikan keandalan studi dankemampuan model untuk digeneralisasikan. Model ensemble: Extra Trees dan LightGBM, mencapai metrik prediktif ROC AUC, akurasi yang hampir sempurna, serta presisi, dan skor F1 yang mendekati 1. LightGBM juga menghasilkan nilai yang kompetitif, menunjukkanefikasi tinggi dari metode ensemble dalam pengaturan data yang kompleks. Hasil-hasil ini dikontraskan dengan metrik kinerja yang signifikan lebih rendah dari DNN, meskipun lebih rendah, dari model tradisional seperti Decision Trees, Random Forest, KNN, dan XGBoost. |\n| Corresponding Author :\u003Cbr>Gregorius Airlangga\u003Cbr>Information System Study Program, Engineering Faculty, Atma Jaya Catholic University of Indonesia, Indonesia\u003Cbr>Jl. Jend. Sudirman No.51 5, RT.004/RW.4, Karet Semanggi, Kecamatan Setiabudi, Kota Jakarta Selatan, Daerah Khusus Ibukota Jakarta 12930\u003Cbr>E-mail: [gregorius.airlangga@atmajaya.ac.id](gregorius.airlangga@atmajaya.ac.id) |  |\n\nINTRODUCTION  \nDiabetes mellitus stands as a formidable challenge in global health, affecting millions worldwide with its chronic implications and serving as a precursor to a range of potentially fatal complications (Lin et al. , 2023; Subramaniyan et al., 2023; Zeinelabdeen et al. , 2024) . The persistent increase in diabetes prevalence, as highlighted by the World Health Organization's reports, underscores the urgency for enhanced diagnostic strategies that can tackle the early detection and management of this disease (Francis et al., 2024; Jayaprabha & Priya, 2024; Motala et al., 2022) . The prevalence of diabetes and its burden on health systems makes it imperative to ","cbCaidfU4xsAww3H","https://ap.wps.com/l/cbCaidfU4xsAww3H","pdf",262547,1,9,"English","en",105,"# Abstract\n# Introduction\n## Diabetes as a public health challenge\n## From classical models to ensemble methods\n## Motivation for LightGBM and Extra Trees comparison","[{\"question\":\"Which machine learning models are compared for diabetes prediction?\",\"answer\":\"The study compares advanced ensemble methods (Extra Trees Classifier and LightGBM) against traditional machine learning algorithms and simpler deep neural network (DNN) architectures.\"},{\"question\":\"What dataset features are used for the diabetes prediction task?\",\"answer\":\"The dataset includes diabetes-relevant features such as glucose concentration, BMI, and insulin levels, among other variables.\"},{\"question\":\"How does the study ensure the reliability of the model evaluation?\",\"answer\":\"It applies polynomial feature transformation and tenfold cross-validation to improve robustness and support generalization on unseen data.\"}]","Comparative Analysis of Machine Learning Models for Predicting Diabetes - Unveiling the Superiority of Advanced Ensemble Methods | PDF",1785727438,23,{"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},"comparative-analysis-of-machine-learning-models-for-predicting-diabetes-unveiling-the-superiority-of-advanced-ensemble-methods","",{"@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/comparative-analysis-of-machine-learning-models-for-predicting-diabetes-unveiling-the-superiority-of-advanced-ensemble-methods/119981/",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":20},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Which machine learning models are compared for diabetes prediction?","Question",{"text":75,"@type":76},"The study compares advanced ensemble methods (Extra Trees Classifier and LightGBM) against traditional machine learning algorithms and simpler deep neural network (DNN) architectures.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What dataset features are used for the diabetes prediction task?",{"text":80,"@type":76},"The dataset includes diabetes-relevant features such as glucose concentration, BMI, and insulin levels, among other variables.",{"name":82,"@type":73,"acceptedAnswer":83},"How does the study ensure the reliability of the model evaluation?",{"text":84,"@type":76},"It applies polynomial feature transformation and tenfold cross-validation to improve robustness and support generalization on unseen data.","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"]