[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-121299-en":3,"doc-seo-121299-105":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},121299,1099514067415,"Rowan","https://ap-avatar.wpscdn.com/avatar/100002539d78ffe74a7?x-image-process=image/resize,m_fixed,w_180,h_180&k=1779092875211072502",7,"Healthcare","Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm","Introduction: Various tools have been developed to determine radiography priority in trauma patients. This study evaluated how machine learning models predict traumatic chest injuries in multiple trauma cases. Methods: Eight machine learning models were trained using demographic characteristics, physical examination findings, and radiologic results from 2860 patients. Results: Most models achieved AUC >0.96 and accuracy up to 0.99, with high sensitivity and specificity. Conclusion: Random Forest and Gradient Boosting showed strong potential for accurate prediction of chest trauma outcomes.","Archives of Academic Emergency Medicine. 2025; 13(1): e41  \n| \u003Cbr>ORIGINAL RESEARCH |  |\n| --- | --- |\n| Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm\u003Cbr>Mohammadhossein Vazirizadeh-mahabadii1,2 , Amir GhaffariJolfayi3 , Mostafa Hosseini4 , Mobina Yarahmadi2 , Hamed Zarei2 , Mohsen Masoodi1 , Arash Sarveazad1,5 ∗ , Mahmoud Yousefifard2†\u003Cbr>1. Colorectal Research Center, Iran University of Medical Sciences, Tehran, Iran\u003Cbr>2. Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran\u003Cbr>3. Rajaie Cardiovascular Medical and Research Center, Iran University of Medical Sciences, Tehran, Iran\u003Cbr>4. Department of Epidemiology and Biostatistics, School of Public Health, Tehran University of Medical Sciences, Tehran, Iran\u003Cbr>5. Nursing Care Research Center, Iran University of Medical Sciences, Tehran, Iran\u003Cbr>Received: January 2025; Accepted: February 2025; Published online: 17 March 2025 |  |\n| Abstract: | Introduction: Various tools have been developed to determine the priority of radiography in trauma patients. This study aimed to investigate the role of machine learning models in predicting chest injuries following multiple trauma. Methods: We used the database of a comprehensive cross-sectional survey conducted in 2015 . Eight machine learning models were developed using demographic characteristics, physical exam findings, and radiologic results of 2860 pa- |\n| tients. Results: Area under the receiver operating characteristic curve (AUC) was greater than 0.96 in Random Forest, Gradient Boosting, XGBoost, Decision Tree, Support Vector Machine (SVM), Logistic Regression, K-Nearest Neighbors (KNN), and Neural Network models. The random forest model, XGBoost and Gradient Boosting had the highest accuracy (0.99) . Sensitivity was also highest in the Gradient Boosting, XGBoost and KNN models (0.99) . The specificity of all of the models in predicting chest radiography outcomes of multiple trauma patients was higher than 0.97, except for logistic regression and SVM (0.912 and 0.885 respectively) . Conclusion: Our study highlights the strong potential of machine learning models, especially Random Forest and Gradient Boosting, in predicting chest trauma outcomes with high accuracy and sensitivity.\u003Cbr>Keywords: Multiple trauma; Thoracic injuries; Machine learning algorithms; Radiography, thoracic; Detection algorithms; Lung injury |  |\n|  Cite this article as:  Vazirizadeh-mahabadi M, Ghaffari JolfayiA, Hosseini M, et al. Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm. Arch Acad Emerg Med. 2025; 13(1): e41. [https://doi.org/10.22037/aaemj.v13i1.2512](https://doi.org/10.22037/aaemj.v13i1.2512) .\u003Cbr>1. Introduction\u003Cbr>Injury-related trauma is the primary cause of death, hospitalization, and disability globally (1) . It accounts for 10% of deaths worldwide and is responsible for 90% of fatalities in Low and Middle-Income Countries (2, 3) . Multiple trauma describes injuries that affect more than two anatomical regions or organs, with at least one injury being potentially lifethreatening. In various studies, multiple trauma is typically defined by an Injury Severity Score (ISS) above 16 and the involvement of significant injuries (with an Abbreviated Injury Scale score greater than 3) in at least two body areas (4) .\u003Cbr>∗ Corresponding Author: Arash Sarveazad; Colorectal Research Center, Rasoul-e-Akram Hospital, Nyaiesh Ave., Tehran, Iran Tel/Fax: +982166554790, Email: [Arashsarveazad@gmail.com](Arashsarveazad@gmail.com), ORCID: [https://orcid.org/0000-0001-](https://orcid.org/0000-0001-)[ ](https://orcid.org/0000-0001-)[9273-1940.](9273-1940.)\u003Cbr>†Corresponding Author: Mahmoud Yousefifard; Physiology Research Center, Iran University of Medical Sciences, Tehran, Iran. Phone/Fax: +982186704771, Email: yousefifard20@gmail.com, ORCID: https://orcid.org/0000-0001-5181- 4985.\u003Cbr>Thoracic trauma is a critical concern, varying widely in severity. Severe th","cbCaivyeLjO78Qhf","https://ap.wps.com/l/cbCaivyeLjO78Qhf","pdf",468714,1,11,"English","en",105,"# Introduction\n# Methods\n# Results\n# Conclusion\n# Keywords","[{\"question\":\"What was the purpose of this study?\",\"answer\":\"The study aimed to investigate the role of machine learning models in predicting traumatic chest injuries following multiple trauma.\"},{\"question\":\"What data were used to train the machine learning models?\",\"answer\":\"Models were developed using demographic characteristics, physical exam findings, and radiologic results from 2860 patients.\"},{\"question\":\"Which models performed best in predicting chest injuries?\",\"answer\":\"Random Forest, XGBoost, and Gradient Boosting achieved the highest accuracy (0.99), and sensitivity was highest in Gradient Boosting, XGBoost, and KNN (0.99).\"}]","Predicting the Presence of Traumatic Chest Injuries Using Machine Learning Algorithm | PDF",1785734968,28,{"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},"predicting-the-presence-of-traumatic-chest-injuries-using-machine-learning-algorithm","",{"@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/healthcare/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/predicting-the-presence-of-traumatic-chest-injuries-using-machine-learning-algorithm/121299/",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 was the purpose of this study?","Question",{"text":75,"@type":76},"The study aimed to investigate the role of machine learning models in predicting traumatic chest injuries following multiple trauma.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"What data were used to train the machine learning models?",{"text":80,"@type":76},"Models were developed using demographic characteristics, physical exam findings, and radiologic results from 2860 patients.",{"name":82,"@type":73,"acceptedAnswer":83},"Which models performed best in predicting chest injuries?",{"text":84,"@type":76},"Random Forest, XGBoost, and Gradient Boosting achieved the highest accuracy (0.99), and sensitivity was highest in Gradient Boosting, XGBoost, and KNN (0.99).","https://schema.org",{"og:url":52,"og:type":87,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":89,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":92},[93,97,101,105,110,115,118,123,128,131,135],{"id":20,"doc_module":4,"doc_module_name":46,"category_name":94,"show_sort_weight":95,"slug":96},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":98,"show_sort_weight":99,"slug":100},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":102,"show_sort_weight":103,"slug":104},"Exam",70,"exam",{"id":106,"doc_module":4,"doc_module_name":46,"category_name":107,"show_sort_weight":108,"slug":109},5,"Comic",60,"comic",{"id":111,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},6,"Technology",50,"technology",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":116,"slug":117},40,"healthcare",{"id":119,"doc_module":4,"doc_module_name":46,"category_name":120,"show_sort_weight":121,"slug":122},8,"Research & Report",30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":126,"slug":127},9,"Religion & Spirituality",20,"religion-spirituality",{"id":126,"doc_module":4,"doc_module_name":46,"category_name":129,"show_sort_weight":126,"slug":130},"World Cup","world-cup",{"id":132,"doc_module":4,"doc_module_name":46,"category_name":133,"show_sort_weight":132,"slug":134},10,"Lifestyle","lifestyle",{"id":136,"doc_module":4,"doc_module_name":46,"category_name":137,"show_sort_weight":106,"slug":138},19,"General","general"]