[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125238-en":3,"doc-seo-125238-105":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},125238,1099514067438,"River Wang","https://ap-avatar.wpscdn.com/avatar/100002539ee87300030?x-image-process=image/resize,m_fixed,w_180,h_180&k=1780474512215547542",8,"Research & Report","A Comparative Analysis of the Performance of Parallel Ensemble and Sequential Ensemble Machine Learning Methods in the Detection of Diabetes Miletus - read online free","Diabetes Mellitus remains a major global cause of mortality, driving demand for faster diagnostic tools for an incurable condition. The study compares parallel and sequential ensemble machine learning approaches for diabetes mellitus detection, evaluating base learners and combined models using an ensemble framework. Parallel ensembles include Random Forest, J48, CART, and Decision Stump, while sequential ensembles include XGBoost, AdaBoostM1, and Gradient Boosting. Results based on UCI data using 70% training and 30% testing with 5-fold cross validation show sequential ensembles achieve about 6% higher classification accuracy, and XGBoost is about 4% better with 10-fold CV, supporting robust early diagnosis and reduced healthcare burden.","[Available online at](Available online at www.sciencedirect.com)[ www.sciencedirect.com](Available online at www.sciencedirect.com)  \nScienceDirect  \nProcedia Computer Science 258 (2025) 1038–1049  \nInternational Conference on Machine Learning and Data Engineering  \nA Comparative Analysis of the Performance of Parallel Ensemble and Sequential Ensemble Machine Learning Methods in the Detection of Diabetes Miletus  \nBlessing Oluwatobi Olorunfemia, Abidemi Emmanuel Adeniyib *, Adewale Opeoluwa Ogundec, Israel Korede Adeyanjud, Federick Oscare, Nabeela T. Adebolaf  \na,cDepartment of Computer Science, Redeemers University, Ede, Osun State, Nigeria.  \nbDepartment of Computer Science, Bowen University, Iwo, Nigeria.  \ndSheffield Hallam University, United Kingdom.  \neAdioo Technology, Abuja, Nigeria.  \nfData Science Department, University of Salford, United Kingdom.  \nAbstract  \nDiabetes Mellitus still forms a major cause of death rates soaring around the globe, heightening scares regarding shooting up diabetic population in the world; and hence straining health attendants to seek for rapid diagnostic tools specific to an incurable disease as described. Many models have been presented for machine learning as base learners, or else combined ensemble techniques. The performance of parallel and sequential ensemble machine learning approaches in the detection of diabetes mellitus: A comparative study, the parallel ensemble methods include Random Forest, J48, CART and Decision Stump (DS) classifiers and the sequential ensemble method includes XGBoost AdaBoostM1 Gradient Boosting. The data set was 70% training and 30 % testing using the dataset on UCI machine repository site. Python analysis using Jupyter Notebook of this model confirmed that sequential ensemble has a classification accuracy about 6% more than parallel method using the same dataset by applying the 5-fold Cross Validation (CV) technique. XGBoost was also 4% better than 10-fold CV. Sequential machine learning models perform better in predicting diabetes mellitus as per the results. Therefore, the study concludes that sequential ensemble approaches are robust and effective in enhancing early diagnosis of patients. Thus, these models can be employed to develop prospective diabetes mellitus detection systems which in turn contributes to better health outcomes and decreasing the load on healthcare .  \n© 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the International Conference on Machine Learning and Data Engineering Keywords: Classification, Diabetes, Sequential Ensemble Method, Parallel Ensemble Methods, machine learning.  \n* Corresponding author. Tel.: +234 8064056436.  \n[E-mail address:](E-mail address: abidemi.adeniyi@bowen.edu.ng)[ abidemi.adeniyi@bowen.edu.ng](E-mail address: abidemi.adeniyi@bowen.edu.ng)  \n1877-0509 © 2025 The Authors. Published by Elsevier B.V.  \nThis is an open access article under the CC BY-NC-ND license ([https://creativecommons.org/licenses/by-nc-nd/4.0](https://creativecommons.org/licenses/by-nc-nd/4.0))  \nPeer-review under responsibility of the scientific committee of the International Conference on Machine Learning and Data Engineering  \n10.1016/j.procs.2025.04.340  \nBlessing Oluwatobi Olorunfemi et al. / Procedia Computer Science 258 (2025) 1038–1049 1039  \n1. Introduction  \nDiabetes Mellitus (DM) is fast emerging as a global health challenge. This is because, in DM insulin is not secreted sufficiently or the body’s response to insulin is inadequate resulting in chronic elevation of blood sweets levels [1] . There has been an explosive increase of DM throughout the world with projections of the number of DM patients increasing from 425 million in 2017 to 629 million by 2045 as reported by the World Health Organization","cbCaikKCqvIoWTub","https://ap.wps.com/l/cbCaikKCqvIoWTub","pdf",921998,1,12,"English","en",105,"# Introduction\n## Diabetes mellitus as a global health challenge\n## Ensemble learning approaches for predictive modelling\n## Study objectives and dataset setup","[{\"question\":\"What ensemble learning methods are compared for diabetes detection?\",\"answer\":\"The study compares parallel ensemble methods (Random Forest, J48, CART, Decision Stump) with sequential ensemble methods (XGBoost, AdaBoostM1, Gradient Boosting).\"},{\"question\":\"How is the dataset used for training and testing?\",\"answer\":\"The dataset is split into 70% for training and 30% for testing, and experiments use cross validation (5-fold and 10-fold) to evaluate performance.\"},{\"question\":\"Which approach achieves better classification accuracy and by how much?\",\"answer\":\"Sequential ensemble models perform better, achieving about 6% higher classification accuracy than parallel methods under the same dataset and 5-fold cross validation; XGBoost is about 4% better under 10-fold CV.\"}]","A Comparative Analysis of the Performance of Parallel Ensemble and Sequential Ensemble Machine Learning Methods in the Detection of Diabetes Miletus - read online free | PDF",1785897661,30,{"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-comparative-analysis-of-the-performance-of-parallel-ensemble-and-sequential-ensemble-machine-learning-methods-in-the-detection-of-diabetes-miletus-read-online-free","",{"@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-comparative-analysis-of-the-performance-of-parallel-ensemble-and-sequential-ensemble-machine-learning-methods-in-the-detection-of-diabetes-miletus-read-online-free/125238/",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 ensemble learning methods are compared for diabetes detection?","Question",{"text":75,"@type":76},"The study compares parallel ensemble methods (Random Forest, J48, CART, Decision Stump) with sequential ensemble methods (XGBoost, AdaBoostM1, Gradient Boosting).","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How is the dataset used for training and testing?",{"text":80,"@type":76},"The dataset is split into 70% for training and 30% for testing, and experiments use cross validation (5-fold and 10-fold) to evaluate performance.",{"name":82,"@type":73,"acceptedAnswer":83},"Which approach achieves better classification accuracy and by how much?",{"text":84,"@type":76},"Sequential ensemble models perform better, achieving about 6% higher classification accuracy than parallel methods under the same dataset and 5-fold cross validation; 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