[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127703-en":3,"doc-seo-127703-105":31,"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":21,"is_downloadable":21,"audit_status":21,"page_count":22,"language":23,"language_code":24,"site_id":25,"html_lang":24,"table_of_contents":26,"faqs":27,"seo_title":28,"seo_description":14,"update_tm":29,"read_time":30},127703,962084928432,"Emma Wilson","https://ap-avatar.wpscdn.com/davatar_155a257f0dc6eb9ab79c44ca47cae57d",8,"Research & Report","Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation","Peripheral vascular disease (PVD) poses a major macrovascular threat to patients with type 2 diabetes mellitus (T2DM), making early risk prediction essential for clinical decision-making. A retrospective cohort of 4,372 inpatients was analyzed using electronic health records, with preprocessing, recursive feature elimination, and training/testing split. Six machine learning models were optimized via grid search and 10-fold cross-validation, while SHAP provided interpretability for the best-performing approach.","TYPE Original Research PUBLISHED 28 February 2024 DOI 10.3389/fendo.2024.1320335  \nOPEN ACCESS  \nEDITED BY  \nBert B. Little,  \nUniversity of Louisville, United States  \nREVIEWED BY  \nJorge Francisco Gomez Cerezo, Universidad Europeas de Madrid, Spain Rasoul Goli,  \nUrmia University of Medical Sciences, Iran  \n*CORRESPONDENCE Feng Ju  \n [hy0208014@hainmc.edu.cn](hy0208014@hainmc.edu.cn)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 12 October 2023  \nACCEPTED 06 February 2024  \nPUBLISHED 28 February 2024  \nCITATION  \nLiu L, Bi B, Cao L, Gui M and Ju F (2024) Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation.  \nFront. Endocrinol. 15:1320335 .  \ndoi: 10.3389/fendo.2024.1320335  \nCOPYRIGHT  \n© 2024 Liu, Bi, Cao, Gui and Ju. 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.  \nPredictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley  \nadditive explanation  \nLianhua Liu 1†, Bo Bi 1†, Li Cao 1, Mei Gui 1 and Feng Ju 2*  \n1 International School of Public Health and One Health, Hainan Medical University, Haikou, Hainan, China, 2 Department of Endocrinology, Second Affiliated Hospital of Hainan Medical University, Haikou, Hainan, China  \nBackground: Peripheral vascular disease (PVD) is a common complication inpatients with type 2 diabetes mellitus (T2DM) . Early detection or prediction the risk of developing PVD is important for clinical decision-making.  \nPurpose: This study aims to establish and validate PVD risk prediction models and perform risk factor analysis for PVD in patients with T2DM using machine learning and Shapley Additive Explanation(SHAP) based on electronic health records.  \nMethods: We retrospectively analyzed the data from 4,372 inpatients with diabetes in a hospital between January 1, 2021, and March 28, 2023 . The data comprised demographic characteristics, discharge diagnoses and biochemical index test results. After data preprocessing and feature selection using Recursive Feature Elimination(RFE), the dataset was split into training and testing sets at a ratio of 8:2, with the Synthetic Minority Over-sampling Technique(SMOTE) employed to balance the training set. Six machine learning(ML) algorithms, including decision tree (DT), logistic regression (LR), random forest (RF), support vector machine(SVM), extreme gradient boosting (XGBoost) and Adaptive Boosting(AdaBoost) were applied to construct PVD prediction models. A grid search with 10-fold cross-validation was conducted to optimize the hyperparameters. Metrics such as accuracy, precision, recall, F1-score, Gmean, and the area under the receiver operating characteristic curve (AUC) assessed the models’ effectiveness. The SHAP method interpreted the bestperforming model.  \nResults: RFE identified the optimal 12 predictors. The XGBoost model outperformed other five ML models, with an AUC of 0 . 945, G-mean of 0 . 843, accuracy of 0 . 890, precision of 0 . 930, recall of 0 . 927, and F1-score of 0 .928. The feature importance of ML models and SHAP results indicated that Hemoglobin (Hb), age, total bile acids (TBA) and lipoprotein(a)(LP-a) are the top four important risk factors for PVD in T2DM.  \nFrontiers in Endocrinology 01 [frontiersin.org](frontiersin.org)  \nConclusion: The machine learning approach successfully developed a PVD risk prediction model with good performance. The model identified the factors associated with PVD and offered phy","cbCaipcSLO1TzzXc","https://ap.wps.com/l/cbCaipcSLO1TzzXc","pdf",2496093,3,1,12,"English","en",105,"# Background\n# Purpose\n# Methods\n## Dataset and preprocessing\n## Modeling and evaluation\n## Interpretability with SHAP\n# Results\n# Conclusion","[{\"question\":\"What problem does the study address for patients with type 2 diabetes?\",\"answer\":\"The study targets early prediction of peripheral vascular disease (PVD) risk in patients with type 2 diabetes mellitus (T2DM) to support clinical decisions.\"},{\"question\":\"How were the predictive models built and evaluated?\",\"answer\":\"The researchers used electronic health records from 4,372 inpatients, applied preprocessing and recursive feature elimination, balanced the training set with SMOTE, and trained six machine learning algorithms. Hyperparameters were optimized using grid search with 10-fold cross-validation, and performance was assessed using metrics such as accuracy, precision, recall, F1-score, G-mean, and AUC.\"},{\"question\":\"Which model performed best and what key risk factors were identified?\",\"answer\":\"The XGBoost model performed best with the highest reported AUC. SHAP and feature-importance results highlighted hemoglobin (Hb), age, total bile acids (TBA), and lipoprotein(a) (LP-a) as top risk factors for PVD in T2DM.\"}]","Predictive model and risk analysis for peripheral vascular disease in type 2 diabetes mellitus patients using machine learning and shapley additive explanation | PDF",1785941028,30,{"code":4,"msg":32,"data":33},"ok",{"site_id":25,"language":24,"slug":34,"title":13,"keywords":35,"description":14,"schema_data":36,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":29},"predictive-model-and-risk-analysis-for-peripheral-vascular-disease-in-type-2-diabetes-mellitus-patients-using-machine-learning-and-shapley-additive-explanation","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,49,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":48},"https://docshare.wps.com/document/","Document",2,{"item":50,"name":12,"@type":44,"position":20},"https://docshare.wps.com/document/research-report/",{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/predictive-model-and-risk-analysis-for-peripheral-vascular-disease-in-type-2-diabetes-mellitus-patients-using-machine-learning-and-shapley-additive-explanation/127703/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":24,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":42,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",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 problem does the study address for patients with type 2 diabetes?","Question",{"text":76,"@type":77},"The study targets early prediction of peripheral vascular disease (PVD) risk in patients with type 2 diabetes mellitus (T2DM) to support clinical decisions.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were the predictive models built and evaluated?",{"text":81,"@type":77},"The researchers used electronic health records from 4,372 inpatients, applied preprocessing and recursive feature elimination, balanced the training set with SMOTE, and trained six machine learning algorithms. Hyperparameters were optimized using grid search with 10-fold cross-validation, and performance was assessed using metrics such as accuracy, precision, recall, F1-score, G-mean, and AUC.",{"name":83,"@type":74,"acceptedAnswer":84},"Which model performed best and what key risk factors were identified?",{"text":85,"@type":77},"The XGBoost model performed best with the highest reported AUC. SHAP and feature-importance results highlighted hemoglobin (Hb), age, total bile acids (TBA), and lipoprotein(a) (LP-a) as top risk factors for PVD in T2DM.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":25},{"code":4,"msg":5,"data":93},[94,98,102,106,111,116,121,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":48,"doc_module":4,"doc_module_name":47,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":47,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":47,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":112,"doc_module":4,"doc_module_name":47,"category_name":113,"show_sort_weight":114,"slug":115},6,"Technology",50,"technology",{"id":117,"doc_module":4,"doc_module_name":47,"category_name":118,"show_sort_weight":119,"slug":120},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":30,"slug":122},"research-report",{"id":124,"doc_module":4,"doc_module_name":47,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":47,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":47,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":47,"category_name":137,"show_sort_weight":107,"slug":138},19,"General","general"]