[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120434-en":3,"doc-seo-120434-105":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},120434,962075114765,"Quinn","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Machine Learning Models for Metabolic Syndrome Identification with Explainable AI - Hybrid XGBoost and K-Means Approach","Metabolic syndrome combines interrelated risks such as hypertension, dyslipidemia, central obesity, and insulin resistance, substantially raising cardiovascular disease and type 2 diabetes likelihood. Early hypertension identification is crucial for timely intervention and effective management. This research proposes a hybrid machine learning framework that couples XGBoost classification with K-Means clustering to strengthen hypertension prediction and reveal patient subgroups from metabolic risk factors. Using 1,878 patient records, the model is evaluated with accuracy, precision, recall, F1-score, and ROC-AUC, reaching 98% accuracy and ROC-AUC of 1.00, while clustering produces five distinct metabolic profiles.","Machine Learning Models for Metabolic Syndrome Identification with  \nExplainable AI  \nEgga Asoka1,2, Fathoni*3, Anggina Primanita4, Indra Griha Tofik Isa5  \n1Doctoral Program in Engineering Science, Sriwijaya University, Indonesia 2Management Informatics, Politeknik Negeri Sriwijaya, Indonesia  \n3,4Computer Science, Sriwijaya University, Indonesia  \n5Prospective Technology of Electrical Engineering and Computer Science, National Chin-Yi  \nUniversity of Technology, Taiwan  \n[Email:](Email:3fathoni@unsri.ac.id)[3](Email:3fathoni@unsri.ac.id)[fathoni@unsri.ac.id](Email:3fathoni@unsri.ac.id)  \nReceived : Feb 16, 2025; Revised : Mar 19, 2025; Accepted : May 7, 2025; Published : Jun 10, 2025  \nAbstract  \n\n| Metabolic syndrome (MetS) is a cluster of interrelated risk factors, including hypertension, dyslipidemia, central obesity, and insulin resistance, significantly increasing the likelihood of cardiovascular diseases and type 2 diabetes. Early identification of hypertension, a key component of MetS, is essential for timely intervention and effective disease management. This research aims to develop a hybrid machine learning model that integrates XGBoost classification with K-Means clustering to enhance or strengthening of hypertension prediction and identify distinct patient subgroups based on metabolic risk factors. The dataset consists of 1,878 patient records with metabolic parameters such as systolic and diastolic blood pressure, fasting glucose, cholesterol levels, and anthropometric measurements. Model performance was assessed using accuracy, precision, recall, F1-score, and ROC-AUC. The proposed XGBoost model achieved an outstanding classification performance with 98% accuracy, 98% precision, 98% recall, 98% F1-score, and an ROC-AUC of 1.00. K-Means clustering further identified five distinct patient subgroups with varying metabolic risk profiles. The findings underscore the potential of machine learning-driven decision support systems in improving hypertension diagnosis and MetS management.\u003Cbr>Keywords : Explainable AI, Hypertension, K-Means, Machine Learning, Metabolic Syndrome, XGBoost. |\n| --- |\n| This work is an open access article and licensed under a Creative Commons Attribution-Non Commercial\u003Cbr>4.0 International License\u003Cbr> |\n\n1. INTRODUCTION  \nMetabolic syndrome, characterised by hypertension, hyperglycemia, dyslipidaemia, and central obesity, elevates the risk of cardiovascular disease, type 2 diabetes, and further metabolic disorders[1] . Due to the often non-specific and gradual onset of metabolic syndrome symptoms, early identification is essential. Hypertension, a principal risk factor for this condition, is characterised by persistently elevated blood pressure, potentially damaging essential organs[2] . This study employs machine learning and explainable AI to identify metabolic syndrome, emphasising hypertension. This study examines a hybrid model that integrates K-Means clustering with XGBoost to tackle the challenge of identifying complex situations involving several risk indicators. K-Means, an unsupervised learning technique, may uncover latent patterns in intricate clinical data, while XGBoost, a robust boosting algorithm, is proficient in classification and prediction tasks[3] . This work aims to develop a model that precisely identifies metabolic syndrome, including hypertension, by integrating the advantages of both algorithmsand offering enhanced understanding of the relationships among its constituent risk factors[4] . Metabolic syndrome (MetS) denotes a collection of risk factors that elevate the likelihood of cardiovascular disease and diabetes. Obesity, especially central obesity, hypertension, dyslipidaemia (elevated triglycerides and diminished HDL cholesterol), insulin resistance, and an unhealthy lifestyle  \nare the predominant risk factors[5] . Central obesity is critical due to the production of adipokines by abdominal adipose tissue, which fosters inflammation and metabolic dysfunction [6","cbCaipdS9U8t6krC","https://ap.wps.com/l/cbCaipdS9U8t6krC","pdf",732222,1,14,"English","en",105,"# Abstract\n# Introduction\n## Metabolic syndrome and hypertension risk\n## Hybrid model: K-Means + XGBoost\n## K-Means clustering (concept and limitations)\n## XGBoost boosting (role in classification)","[{\"question\":\"What problem does the hybrid model target?\",\"answer\":\"It targets early identification of metabolic syndrome with an emphasis on hypertension, using both classification and subgroup discovery. The hybrid design improves prediction and helps interpret relationships among risk factors.\"},{\"question\":\"How does the approach combine XGBoost and K-Means?\",\"answer\":\"K-Means clustering groups patients into distinct subgroups based on metabolic risk factors, while XGBoost performs classification and prediction for hypertension-related outcomes. Together they enhance performance and support understanding of risk-factor patterns.\"},{\"question\":\"What dataset and evaluation metrics are used?\",\"answer\":\"The dataset contains 1,878 patient records with metabolic parameters such as blood pressure, fasting glucose, cholesterol, and anthropometric measurements. Model performance is assessed using accuracy, precision, recall, F1-score, and ROC-AUC.\"}]","Machine Learning Models for Metabolic Syndrome Identification with Explainable AI - Hybrid XGBoost and K-Means Approach | PDF",1785730093,35,{"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},"machine-learning-models-for-metabolic-syndrome-identification-with-explainable-ai-hybrid-xgboost-and-k-means-approach","",{"@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/machine-learning-models-for-metabolic-syndrome-identification-with-explainable-ai-hybrid-xgboost-and-k-means-approach/120434/",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},"What problem does the hybrid model target?","Question",{"text":75,"@type":76},"It targets early identification of metabolic syndrome with an emphasis on hypertension, using both classification and subgroup discovery. The hybrid design improves prediction and helps interpret relationships among risk factors.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How does the approach combine XGBoost and K-Means?",{"text":80,"@type":76},"K-Means clustering groups patients into distinct subgroups based on metabolic risk factors, while XGBoost performs classification and prediction for hypertension-related outcomes. Together they enhance performance and support understanding of risk-factor patterns.",{"name":82,"@type":73,"acceptedAnswer":83},"What dataset and evaluation metrics are used?",{"text":84,"@type":76},"The dataset contains 1,878 patient records with metabolic parameters such as blood pressure, fasting glucose, cholesterol, and anthropometric measurements. 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