[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-127917-en":3,"doc-seo-127917-105":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},127917,137451207643,"Noah","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","Predicting stroke severity of patients using interpretable machine learning algorithms - Research summary","Stroke remains a major global health challenge, driving high mortality and creating substantial costs for healthcare systems, especially in low- and middle-income settings. Timely and reliable assessment of stroke severity is essential for forecasting clinical outcomes. This study trains multiple interpretable machine learning models to predict stroke severity using two clinical scoring systems, RACE and NIHSS, leveraging SHAP to quantify feature-level contributions and strengthen clinical interpretability.","University of Southern Denmark  \nPredicting stroke severity of patients using interpretable machine learning algorithms  \nSorayaie Azar, Amir; Samimi, Tahereh; Tavassoli, Ghanbar; Naemi, Amin; Rahimi, Bahlol; Hadianfard, Zahra; Wiil, Uffe Kock; Nazarbaghi, Surena; Bagherzadeh Mohasefi, Jamshid; Lotfnezhad Afshar, Hadi  \nPublished in:  \nEuropean journal of medical research  \nDOI:  \n10.1186/s40001-024-02147-1  \nPublication date: 2024  \nDocument version:  \nFinal published version  \nDocument license: CC BY-NC-ND  \nCitation for pulished version (APA):  \nSorayaie Azar, A. , Samimi, T. , Tavassoli, G. , Naemi, A. , Rahimi, B. , Hadianfard, Z. , Wiil, U. K. , Nazarbaghi, S. , Bagherzadeh Mohasefi, J. , & Lotfnezhad Afshar, H. (2024) . Predicting stroke severity of patients using interpretable machine learning algorithms. European journal of medical research, 29(1), Article 547. [https://doi.org/10.1186/s40001-024-02147-1](https://doi.org/10.1186/s40001-024-02147-1)  \nGo to publication entry in University of Southern Denmark's Research Portal  \nTerms of use  \nThis work is brought to you by the University of Southern Denmark.  \nUnless otherwise specified it has been shared according to the terms for self-archiving.  \nIf no other license is stated, these terms apply:  \n• You may download this work for personal use only.  \n• You may not further distribute the material or use it for any profit-making activity or commercial gain  \n• You may freely distribute the URL identifying this open access version  \nIf you believe that this document breaches copyright please contact us providing details and we will investigate your claim. Please direct all enquiries to [puresupport@bib.sdu.dk](puresupport@bib.sdu.dk)  \nDownload date: 05. Aug. 2026  \nSorayaie Azar etal. European Journal  \nEuropean Journal of Medical Research (2024) 29:547  \n[https://doi.org/10.1186/s40001-024-02147-1](https://doi.org/10.1186/s40001-024-02147-1 of Medical Research)[ of Medical Research](https://doi.org/10.1186/s40001-024-02147-1 of Medical Research)  \n RESEARCH Open Access  \nPredicting stroke severity of patients using interpretable machine learning algorithms  \nAmir Sorayaie Azar1,2†, Tahereh Samimi3,4†, Ghanbar Tavassoli3,4,5†, Amin Naemi 1, Bahlol Rahimi3,4, Zahra Hadianfard3, Uffe Kock Wiil1, Surena Nazarbaghi6, Jamshid Bagherzadeh Mohasefi 1,2* and Hadi Lotfnezhad Afshar3,4*  \nAbstract  \nBackground Stroke is a significant global health concern, ranking as the second leading cause of death and placing a substantial financial burden on healthcare systems, particularly in low-and middle-income countries. Timely evaluation of stroke severity is crucial for predicting clinical outcomes, with standard assessment tools being the Rapid Arterial Occlusion Evaluation (RACE) and the National Institutes of Health Stroke Scale (NIHSS) . This study aims to utilize Machine Learning (ML) algorithms to predict stroke severity using these two distinct scales.  \nMethods We conducted this study using two datasets collected from hospitals in Urmia, Iran, corresponding to stroke severity assessments based on RACE and NIHSS. Seven ML algorithms were applied, including K-Nearest Neighbor (KNN), Decision Tree (DT), Random Forest (RF), Adaptive Boosting (AdaBoost), Extreme Gradient Boosting (XGBoost), Support Vector Machine (SVM), and Artificial Neural Network (ANN) . Hyperparameter tuning was performed using grid search to optimize model performance, and SHapley Additive Explanations (SHAP) were used to interpret the contribution of individual features.  \nResults Among the models, the RF achieved the highest performance, with accuracies of 92 . 68% for the RACE dataset and 91. 19% for the NIHSS dataset. The Area Under the Curve (AUC) was 92. 02% and 97. 86% for the RACE and NIHSS datasets, respectively. The SHAP analysis identified triglyceride levels, length of hospital stay, and age as critical predictors of stroke severity.  \nConclusions This study is the first to apply ML models to the ","cbCaii27x5hA0nqV","https://ap.wps.com/l/cbCaii27x5hA0nqV","pdf",3850857,2,1,24,"English","en",105,"# Abstract\n## Background\n## Methods\n## Results\n## Conclusions","[{\"question\":\"Which clinical scales were used to predict stroke severity?\",\"answer\":\"The study predicts severity using two distinct scales: RACE and NIHSS.\"},{\"question\":\"What machine learning methods were evaluated?\",\"answer\":\"Seven algorithms were tested: KNN, Decision Tree, Random Forest, AdaBoost, XGBoost, SVM, and ANN.\"},{\"question\":\"How was model interpretability achieved?\",\"answer\":\"SHapley Additive Explanations (SHAP) were used to interpret the contribution of individual features to model predictions.\"}]","Predicting stroke severity of patients using interpretable machine learning algorithms - Research summary | PDF",1785942930,60,{"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},"predicting-stroke-severity-of-patients-using-interpretable-machine-learning-algorithms-research-summary","",{"@graph":37,"@context":86},[38,54,69],{"@type":39,"itemListElement":40},"BreadcrumbList",[41,45,48,51],{"item":42,"name":43,"@type":44,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":46,"name":47,"@type":44,"position":20},"https://docshare.wps.com/document/","Document",{"item":49,"name":12,"@type":44,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":44,"position":53},"https://docshare.wps.com/document/predicting-stroke-severity-of-patients-using-interpretable-machine-learning-algorithms-research-summary/127917/",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","2026-08-27","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},"Which clinical scales were used to predict stroke severity?","Question",{"text":76,"@type":77},"The study predicts severity using two distinct scales: RACE and NIHSS.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What machine learning methods were evaluated?",{"text":81,"@type":77},"Seven algorithms were tested: KNN, Decision Tree, Random Forest, AdaBoost, XGBoost, SVM, and ANN.",{"name":83,"@type":74,"acceptedAnswer":84},"How was model interpretability achieved?",{"text":85,"@type":77},"SHapley Additive Explanations (SHAP) were used to interpret the contribution of individual features to model predictions.","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},[94,98,102,106,110,115,120,123,128,131,135],{"id":21,"doc_module":4,"doc_module_name":47,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":20,"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":30,"slug":109},5,"Comic","comic",{"id":111,"doc_module":4,"doc_module_name":47,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":47,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":47,"category_name":12,"show_sort_weight":121,"slug":122},30,"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"]