[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118839-en":3,"doc-seo-118839-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118839,34359740700684,"Finn","https://ap-avatar.wpscdn.com/avatar/1f400023980c374ae676?_k=1777273430885731487",8,"Research & Report","Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease - research summary","Cardiovascular disease remains a major cause of death and disability worldwide, and carotid plaque serves as an important marker reflecting the severity of atherosclerosis. This study develops eight machine learning prediction models using physical examination data from 4,659 patients, optimized with 10-fold cross-validation. Feature importance is quantified and visualized via SHAP to support clinical interpretability. Results show XGBoost achieves the strongest overall discrimination, and key predictors include age, smoking, alcohol intake, and BMI, enabling feasible plaque-risk screening for chronic disease management.","TYPE Original Research PUBLISHED 22 September 2023 DOI 10.3389/fcvm.2023.1178782  \nEDITED BY  \nSperanza Rubattu,  \nSapienza University of Rome, Italy  \nREVIEWED BY  \nSaijun Zhou,  \nTianjin Medical University, China Abhinav Grover,  \nMedical College of Wisconsin, United States Yuqing Xu,  \nZhejiang University, China  \n*CORRESPONDENCE  \nShaorong Yang  \n [w201606202008@163.com](w201606202008@163.com)  \nRECEIVED 06 March 2023  \nACCEPTED 12 September 2023  \nPUBLISHED 22 September 2023  \nCITATION  \nBin C, Li Q, Tang J, Dai C, Jiang T, Xie X, Qiu M, Chen L and Yang S (2023) Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease.  \nFront. Cardiovasc. Med. 10:1178782 .  \ndoi: 10.3389/fcvm.2023.1178782  \nCOPYRIGHT  \n© 2023 Bin, Li, Tang, Dai, Jiang, Xie, Qiu, Chen and Yang. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nMachine learning models for predicting the risk factor of carotid plaque in cardiovascular disease  \nChengling Bin1, Qin Li1, Jing Tang1, Chaorong Dai1, Ting Jiang1, Xiufang Xie2, Min Qiu3, Lumiao Chen4 and Shaorong Yang1*  \n1Health Management Section, The First People’s Hospital of Neijiang, Neijiang, China, 2Department of Respiratory and Critical Care Medicine, The First People’s Hospital of Neijiang, Neijiang, China, 3Special Inspection Department, The First People’s Hospital of Neijiang, Neijiang, China, 4Laboratory Department, The First People’s Hospital of Neijiang, Neijiang, China  \nIntroduction: Cardiovascular disease (CVD) is a group of diseases involving the heart or blood vessels and represents a leading cause of death and disability worldwide. Carotid plaque is an important risk factor for CVD that can reflect the severity of atherosclerosis. Accordingly, developing a prediction model for carotid plaque formation is essential to assist in the early prevention and management of CVD. Methods: In this study, eight machine learning algorithms were established, and their performance in predicting carotid plaque risk was compared. Physical examination data were collected from 4,659 patients and used for model training and validation. The eight predictive models based on machine learning algorithms were optimized using the above dataset and 10-fold cross-validation. The Shapley Additive Explanations (SHAP) tool was used to compute and visualize feature importance. Then, the performance of the models was evaluated according to the area under the receiver operating characteristic curve (AUC), feature importance, accuracy and specificity.  \nResults: The experimental results indicated that the XGBoost algorithm outperformed the other machine learning algorithms, with an AUC, accuracy and specificity of 0 . 808, 0 .749 and 0 .762, respectively. Moreover, age, smoke, alcohol drink and BMI were the top four predictors of carotid plaque formation. It is feasible to predict carotid plaque risk using machine learning algorithms. Conclusions: This study indicates that our models can be applied to routine chronic disease management procedures to enable more preemptive, broad-based screening for carotid plaque and improve the prognosis of CVD patients.  \nKEYWORDS  \nmachine learning, prediction model, carotid plaque, shap, cardiovascular disease  \n1. Introduction  \nCardiovascular disease (CVD) is generally divided into several types, such as coronary heart disease (CHD), cerebrovascular disease, heart failure, hypertension and so on (1) . CVD is one of the leading causes of death worldwide, with 17.9 million deaths in 2016 and predicted to increase to approximately 23.6 million deaths by 2030","cbCaijfVe6cYN2Ya","https://ap.wps.com/l/cbCaijfVe6cYN2Ya","pdf",8087023,1,10,"English","en",105,"# Introduction\n## Cardiovascular disease and carotid plaque\n## Machine learning in medical prediction\n# Methods\n## Dataset and model development\n## Validation strategy and feature importance\n# Results\n## Model performance and top predictors\n# Conclusions","[{\"question\":\"What was the main goal of the study?\",\"answer\":\"To develop and compare eight machine learning models that predict the risk factor of carotid plaque formation in cardiovascular disease, supporting early prevention and management.\"},{\"question\":\"How were the prediction models trained and validated?\",\"answer\":\"Physical examination data from 4,659 patients were used for training and validation, and the models were optimized with 10-fold cross-validation.\"},{\"question\":\"Which model performed best and what were the key predictors?\",\"answer\":\"XGBoost outperformed the other algorithms. The top predictors were age, smoking, alcohol intake, and BMI.\"}]","Machine learning models for predicting the risk factor of carotid plaque in cardiovascular disease - research summary | PDF",1785720553,25,{"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":87,"head_meta":89,"extra_data":91,"updated_unix":28},"machine-learning-models-for-predicting-the-risk-factor-of-carotid-plaque-in-cardiovascular-disease-research-summary","",{"@graph":36,"@context":86},[37,54,69],{"@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-predicting-the-risk-factor-of-carotid-plaque-in-cardiovascular-disease-research-summary/118839/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-04","2026-08-03",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"What was the main goal of the study?","Question",{"text":76,"@type":77},"To develop and compare eight machine learning models that predict the risk factor of carotid plaque formation in cardiovascular disease, supporting early prevention and management.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the prediction models trained and validated?",{"text":81,"@type":77},"Physical examination data from 4,659 patients were used for training and validation, and the models were optimized with 10-fold cross-validation.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what were the key predictors?",{"text":85,"@type":77},"XGBoost outperformed the other algorithms. 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