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This study applied machine learning to improve ASCVD risk assessment beyond traditional risk factor models. Using NHANES data from 2007–2018 with 4,211 adults diagnosed with diabetes or prediabetes, nine models were trained and validated, and evaluated by discrimination, calibration, and clinical net benefit. An SVM-based tool and online platform supported real-time individualized screening for early intervention.",{"@graph":69,"@context":123},[70,84,106],{"@type":71,"itemListElement":72},"BreadcrumbList",[73,77,79,82],{"item":74,"name":75,"@type":76,"position":8},"https://docshare.wps.com","Home","ListItem",{"item":78,"name":9,"@type":76,"position":14},"https://docshare.wps.com/document/",{"item":80,"name":35,"@type":76,"position":81},"https://docshare.wps.com/document/healthcare/",3,{"item":83,"name":65,"@type":76,"position":19},"https://docshare.wps.com/document/machine-learning-based-prediction-of-atherosclerotic-cardiovascular-disease-risk-in-adults-with-diabetes-or-prediabetes/457461/",{"url":83,"name":65,"@type":85,"image":86,"author":91,"headline":65,"publisher":94,"fileFormat":97,"inLanguage":63,"description":67,"dateModified":98,"datePublished":99,"encodingFormat":97,"isAccessibleForFree":100,"interactionStatistic":101},"DigitalDocument",{"url":87,"@type":88,"width":89,"height":90},"https://docshare.wps.com/thumbnails/machine-learning-based-prediction-of-atherosclerotic-cardiovascular-disease-risk-in-adults-with-diabetes-or-prediabetes/457461.png","ImageObject",300,407,{"name":92,"@type":93},"Aran","Person",{"url":74,"name":95,"@type":96},"DocShare","Organization","application/pdf","2026-10-30","2026-09-30",true,{"@type":102,"interactionType":103,"userInteractionCount":105},"InteractionCounter",{"@type":104},"ViewAction",15,{"@type":107,"mainEntity":108},"FAQPage",[109,115,119],{"name":110,"@type":111,"acceptedAnswer":112},"What dataset and study period were used to build the machine learning models?","Question",{"text":113,"@type":114},"The study used NHANES survey data spanning 2007 to 2018, including 4,211 participants diagnosed with diabetes or prediabetes.","Answer",{"name":116,"@type":111,"acceptedAnswer":117},"Which machine learning model showed the strongest predictive performance?",{"text":118,"@type":114},"The SVM model demonstrated strong discrimination, with AUC values of 0.831 in the training set and 0.859 in the validation set.",{"name":120,"@type":111,"acceptedAnswer":121},"Which factors were identified as significantly associated with ASCVD risk?",{"text":122,"@type":114},"Eight variables were reported as significantly associated: age, waist circumference, poverty–income ratio, blood urea nitrogen, total cholesterol, systolic blood pressure, hypertension, and smoking status.","https://schema.org",{"og:url":83,"og:type":125,"og:title":65,"og:site_name":95,"og:description":67},"article",{"robots":127,"canonical":83},"index,follow",{"doc_id":129,"site_id":62},457461,1790812126,{"code":4,"msg":5,"data":132},{"doc_id":129,"user_id":133,"nickname":92,"user_avatar":134,"doc_module":4,"category_id":34,"category_name":35,"doc_title":65,"doc_description":67,"doc_content":135,"file_id":136,"file_url":137,"file_type":138,"file_size":139,"view_count":105,"is_deleted":4,"is_public":8,"is_downloadable":8,"audit_status":8,"page_count":140,"language":141,"language_code":63,"site_id":62,"html_lang":63,"table_of_contents":142,"faqs":143,"seo_title":144,"seo_description":67,"update_tm":145,"read_time":41,"preview_page_count":14,"preview_urls":146},137455076865,"https://ap-avatar.wpscdn.com/davatar_29158cc5080c5b710cf443261637dec0","Li and Wang BMC Cardiovascular Disorders (2026) 26:14 BMC Cardiovascular Disorders  \n[https://doi.org/10.1186/s12872-025-05247-w](https://doi.org/10.1186/s12872-025-05247-w)  \nRESEARCH Open Access  \nMachine learning-based prediction   \nof atherosclerotic cardiovascular disease risk in adults with diabetes or prediabetes  \nYang Li 1 and Bing Wang 1*  \nAbstract  \nBackground Diabetes and prediabetes significantly increase the risk of atherosclerotic cardiovascular disease (ASCVD), posing a major global health challenge. Although traditional ASCVD risk factors have been extensively studied, there is limited research on applying machine learning.  \nMethods This study used data from the NHANES survey spanning 2007 to 2018, including 4,211 participants diagnosed with diabetes or prediabetes. Key variables were identified through univariate and multivariate logistic regression analyses. The dataset was randomly split into training and validation sets at a 7:3 ratio. Nine machine learning models (including CART, SVM, and GBM) were developed and evaluated using AUC, Brier scores, calibration curves, and decision curve analysis. Additionally, an online risk prediction platform was created to provide real-time ASCVD risk assessments, helping clinicians with early screening and intervention.  \nResults Eight variables significantly associated with ASCVD risk were identified through univariate and multivariate logistic regression analyses, including age, waist circumference, poverty–income ratio, blood urea nitrogen, total cholesterol, systolic blood pressure, hypertension, and smoking status. Based on these predictors, the SVM model achieved AUC values of 0.831 in the training set and 0.859 in the validation set, demonstrating excellent discriminative ability. Calibration curves indicated good agreement between predicted and observed risks across different risk levels, while Brier scores further supported the overall predictive accuracy of the model. Decision curve analysis showed that the model provided substantial net clinical benefit. Collectively, these evaluation metrics highlight the strong predictive performance and practical utility of the SVM model.  \nConclusion The SVM model effectively predicts ASCVD risk in individuals with diabetes or prediabetes, emphasizing the importance of managing modifiable risk factors such as dyslipidemia, hypertension, smoking, and abdominal obesity. This model offers an effective tool for individualized risk assessment and early prevention, and the online platform further supports clinical application by enabling early identification and intervention for high-risk populations.  \nKeywords Diabetes, Prediabetes, Atherosclerotic cardiovascular disease, Machine learning, Predictive model  \n*Correspondence: Bing Wang [hnxgwk@126.com](hnxgwk@126.com)  \n1The Fifth Affiliated Hospital of Zhengzhou University, Zhengzhou University, Zhengzhou, China  \n© The Author(s) 2025. Open Access This article is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License, which permits any non-commercial use, sharing, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if you modified the licensed material. You do not have permission under this licence to share adapted material derived from this article or parts of it. The images or other third party material in this article are included in the article’s Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit [http://creati](http://creati)[vecommons.org/l](vecommons.org/l)icenses/by-nc-nd/4.0/.","cbCaieKwgWn0IV8i","https://ap.wps.com/l/cbCaieKwgWn0IV8i","pdf",2244515,12,"English","# Introduction\n# Abstract\n## Background\n## Methods\n## Results\n## Conclusion\n# Keywords","[{\"question\":\"What dataset and study period were used to build the machine learning models?\",\"answer\":\"The study used NHANES survey data spanning 2007 to 2018, including 4,211 participants diagnosed with diabetes or prediabetes.\"},{\"question\":\"Which machine learning model showed the strongest predictive performance?\",\"answer\":\"The SVM model demonstrated strong discrimination, with AUC values of 0.831 in the training set and 0.859 in the validation set.\"},{\"question\":\"Which factors were identified as significantly associated with ASCVD risk?\",\"answer\":\"Eight variables were reported as significantly associated: age, waist circumference, poverty–income ratio, blood urea nitrogen, total cholesterol, systolic blood pressure, hypertension, and smoking status.\"}]","Machine learning-based prediction of atherosclerotic cardiovascular disease risk in adults with diabetes or prediabetes | PDF",1790749387,[147,151],{"page_no":8,"url":148,"width":149,"height":150},"https://res-academy.cache.wpscdn.com/upload/kdocshare/2026-11-08/7de6b049c3771f35e4a2132b6f901daa.webp",983,1310,{"page_no":14,"url":152,"width":153,"height":154},"https://res-academy.cache.wpscdn.com/upload/kdocshare/2026-11-08/1ac50153dddf07c3587365d663db921b.webp",982,1309]