[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120236-en":3,"doc-seo-120236-105":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":4,"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},120236,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",7,"Healthcare","Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients","Severe sepsis in adults is associated with high mortality, creating an urgent need for accurate prognostic prediction models. This study retrospectively analyzes 606 adult sepsis inpatients (2020–2022), comparing traditional logistic regression with multiple machine learning approaches. Univariate screening and LASSO-based variable selection support model development, while receiver operating characteristic, calibration, and decision curve analyses validate performance in a training/validation split. The random forest model achieves the highest discriminative ability and shows advantages over logistic regression and established scoring systems (SOFA, APACHE).","OPEN ACCESS  \nEDITED BY  \nQinghe Meng,  \nUpstate Medical University, United States  \nREVIEWED BY  \nDung Tran,  \nHospices Civils de Lyon, France Mowafaq Salem Alzboon, Jadara University, Jordan  \n*CORRESPONDENCE  \nHongsheng Wu  \n [crazywu2007@126.com](crazywu2007@126.com)[ ](crazywu2007@126.com)Shengmin Zhang  \n [zsmin2008@163.com](zsmin2008@163.com)[ ](zsmin2008@163.com)RECEIVED 17 September 2024 ACCEPTED 10 December 2024 PUBLISHED 06 January 2025  \nCITATION  \nWu H, Liao B, Ji T, Ma K, Luo Y and Zhang S (2025) Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients.  \nFront. Med. 11:1496869.  \ndoi: 10.3389/fmed.2024.1496869  \nCOPYRIGHT  \n© 2025 Wu, Liao, Ji, Ma, Luo and Zhang. 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.  \nTYPE Original Research PUBLISHED 06 January 2025  \nDOI 10.3389/fmed.2024.1496869  \nComparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients  \nHongsheng Wu*, Biling Liao, Tengfei Ji, Keqiang Ma, Yumei Luo and Shengmin Zhang *  \nHepatobiliary Pancreatic Surgery Department, Huadu District People’s Hospital of Guangzhou, Guangzhou, China  \nBackground: Sepsis is a life-threatening disease associated with a high mortality rate, emphasizing the need for the exploration of novel models to predict the prognosis of this patient population. This study compared the performance of traditional logistic regression and machine learning models in predicting adult sepsis mortality.  \nObjective: To develop an optimum model for predicting the mortality of adult sepsis patients based on comparing traditional logistic regression and machine learning methodology.  \nMethods: Retrospective analysis was conducted on 606 adult sepsis inpatients at our medical center between January 2020 and December 2022, who were randomly divided into training and validation sets in a 7:3 ratio. Traditional logistic regression and machine learning methods were employed to assess the predictive ability of mortality in adult sepsis. Univariate analysis identified independent risk factors for the logistic regression model, while Least Absolute Shrinkage and Selection Operator (LASSO) regression facilitated variable shrinkage and selection for the machine learning model. Among various machine learning models, which included Bagged Tree, Boost Tree, Decision Tree, LightGBM, Naïve Bayes, Nearest Neighbors, Support Vector Machine (SVM), and Random Forest (RF), the one with the maximum area under the curve (AUC) was chosen for model construction. Model validation and comparison with the Sequential Organ Failure Assessment (SOFA) and the Acute Physiology and Chronic Health Evaluation (APACHE) scores were performed using receiver operating characteristic (ROC) curves, calibration curves, and decision curve analysis (DCA) curves in the validation set.  \nResults: Univariate analysis was employed to assess 17 variables, namely gender, history of coronary heart disease (CHD), systolic pressure, white blood cell (WBC), neutrophil count (NEUT), lymphocyte count (LYMP), lactic acid, neutrophil-tolymphocyte ratio (NLR), red blood cell distribution width (RDW), interleukin-6 (IL-6), prothrombin time (PT), international normalized ratio (INR), fibrinogen (FBI), D-dimer, aspartate aminotransferase (AST), total bilirubin (Tbil), and lung  \ninfection. Significant differences (p \u003C 0.05) between the survival and non-survival groups were observed for these variables. Utilizing stepwise regression with the“backward” method, independent risk factors, i","cbCaihlvRztgaRvV","https://ap.wps.com/l/cbCaihlvRztgaRvV","pdf",1617038,1,14,"English","en",105,"# Background\n## Objective\n## Methods\n## Results\n## Conclusion\n# Introduction\n## Clinical assessment and scoring systems\n# Keywords","[{\"question\":\"What was the main objective of this study?\",\"answer\":\"To develop an optimal model for predicting mortality in adult sepsis patients by comparing traditional logistic regression with machine learning methods.\"},{\"question\":\"How were the predictive models constructed and selected?\",\"answer\":\"Independent risk factors were identified with univariate analysis and stepwise logistic regression, while machine learning models used LASSO for shrinkage and selection; the best model was chosen based on maximum AUC.\"},{\"question\":\"Which model performed best for mortality prediction?\",\"answer\":\"The random forest model showed the highest area under the curve (AUC) and demonstrated superior performance compared with traditional logistic regression, validated alongside SOFA and APACHE scoring.\"}]","Comparison between traditional logistic regression and machine learning for predicting mortality in adult sepsis patients | PDF",1785728912,35,{"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},"comparison-between-traditional-logistic-regression-and-machine-learning-for-predicting-mortality-in-adult-sepsis-patients","",{"@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/comparison-between-traditional-logistic-regression-and-machine-learning-for-predicting-mortality-in-adult-sepsis-patients/120236/",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":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"What was the main objective of this study?","Question",{"text":75,"@type":76},"To develop an optimal model for predicting mortality in adult sepsis patients by comparing traditional logistic regression with machine learning methods.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"How were the predictive models constructed and selected?",{"text":80,"@type":76},"Independent risk factors were identified with univariate analysis and stepwise logistic regression, while machine learning models used LASSO for shrinkage and selection; 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