[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-128191-en":3,"doc-seo-128191-105":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},128191,2336475104042,"Skyler","https://ap-avatar.wpscdn.com/avatar/22000c4c32af1715be0?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786537525561427321",8,"Research & Report","Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index - a retrospective study","Premature rupture of membranes (PROM) threatens maternal and neonatal outcomes, and clinical risk assessment remains limited. This retrospective study builds and evaluates an interpretable machine learning prediction model for PROM risk using screened variables and quantifies the relationship between PROM and the nutritional inflammatory index. Ridge regression and Boruta-selected features feed multiple algorithms, with selection and performance judged by ROC-AUC and classification metrics. SHAP-derived contributions support a nomogram for clinical interpretation and decision support.","TYPE Original Research PUBLISHED 18 June 2025  \nDOI 10.3389/fmed.2025.1557919  \nOPEN ACCESS  \nEDITED BY  \nMaria Luisa Ojeda,  \nUniversity of Seville, Spain  \nREVIEWED BY  \nTakuya Kondo,  \nKyushu University, Japan Yefang Huang,  \nHospital of Chengdu University of Traditional Chinese Medicine, China  \nAkihiro Yasui,  \nNagoya University, Japan  \n*CORRESPONDENCE  \nQiulan Yu  \n [18914631090@163.com](18914631090@163.com)  \nRECEIVED 09 January 2025  \nACCEPTED 20 May 2025  \nPUBLISHED 18 June 2025  \nCITATION  \nZheng M, Zhang X, Wang H, Yuan P and Yu Q (2025) Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospective study.  \nFront. Med. 12:1557919 .  \ndoi: 10.3389/fmed.2025.1557919  \nCOPYRIGHT  \n© 2025 Zheng, Zhang, Wang, Yuan and Yu. 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.  \nInterpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index: a retrospective study  \nMeng Zheng, Xiaowei Zhang, Haihong Wang, Ping Yuan and Qiulan Yu*  \nDepartment of Obstetrics and Gynecology, Binhai County People’s Hospital, Yancheng, Jiangsu, China  \nBackground: Premature rupture of membranes (PROM) poses significant risks to both maternal and neonatal health. This study aims to construct a risk factor prediction model related to PROM by using machine learning technology and explore the association with nutritional inflammatory index.  \nMethods: A retrospective analysis was conducted on patients with PROM. Based on the variables screened out by ridge regression and Boruta algorithm, univariate and multivariate logistic regression analyses were further adopted. According to the sample data, it is divided into the training set and the internal validation set in a ratio of 7:3 . The research group adopted four machine learning algorithms: Extreme Gradient Boost (XGBoost), Support Vector Machine (SVM), Logistic Regression (LR), and Random Forest (RF) . The selected variables were incorporated into model construction, with the area under the receiver operating characteristic (ROC) curve (AUC) serving as a criterion for model selection. Model performance was assessed using AUC values, sensitivity, specificity, recall, F1 score, and accuracy. The variables were selected based on the contribution degree of the variables in Shapley additive Interpretation (SHAP) to construct the nomogram.  \nResults: A retrospective analysis was conducted involving 800 parturients at Binhai County People’s Hospital from January 2023 to October 2024, comprising 400 with PROM and 400 with normal delivery. The RF model demonstrated superior performance with an AUC of 0.757, sensitivity of 67.4%, and specificity of 65 . 1% . Key predictive factors identified included body mass index (BMI), prognostic nutritional index (PNI), platelet, albumin, and aggregate index of systemic inflammation (AISI) . The ROC of the model also showed good efficacy, with an AUC of 0 .777.  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \nConclusion: This study highlights the potential of machine learning in enhancing the understanding and prediction of PROM, and emphasizes the significance of inflammatory and nutritional indicators, paving the way for future research in maternal-fetal medicine.  \nKEYWORDS  \nPROM, machine learning, nomogram, nutritional inflammation index, predictive models  \n1 Introduction  \nPremature rupture of membranes (PROM) is a well-known risk factor for pre","cbCaim5EKY8YgRxw","https://ap.wps.com/l/cbCaim5EKY8YgRxw","pdf",1969784,2,1,12,"English","en",105,"# Background\n## Systemic inflammation and nutritional indicators\n# Methods\n## Study design and data splitting\n## Feature selection and statistical modeling\n## Machine learning algorithms and evaluation\n## SHAP interpretation and nomogram\n# Results\n## Dataset and model performance\n## Key predictive factors\n# Conclusion","[{\"question\":\"What is the purpose of the interpretable machine learning model in this study?\",\"answer\":\"The study aims to construct a risk factor prediction model for PROM using machine learning and to examine how PROM relates to the nutritional inflammatory index.\"},{\"question\":\"How were predictive variables selected and models evaluated?\",\"answer\":\"Variables were screened using ridge regression and Boruta, then incorporated into multiple machine learning models. Model selection and evaluation used ROC AUC and additional metrics such as sensitivity, specificity, recall, F1 score, and accuracy.\"},{\"question\":\"Which factors were identified as key predictors of PROM?\",\"answer\":\"The study reports key predictive factors including BMI, PNI, platelet, albumin, and AISI, with model performance assessed through ROC characteristics.\"}]","Interpretable machine learning model for identification and risk factor of premature rupture of membranes (PROM) and its association with nutritional inflammatory index - a retrospective study | PDF",1785945443,30,{"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},"interpretable-machine-learning-model-for-identification-and-risk-factor-of-premature-rupture-of-membranes-prom-and-its-association-with-nutritional-inflammatory-index-a-retrospective-study","",{"@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/interpretable-machine-learning-model-for-identification-and-risk-factor-of-premature-rupture-of-membranes-prom-and-its-association-with-nutritional-inflammatory-index-a-retrospective-study/128191/",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},"What is the purpose of the interpretable machine learning model in this study?","Question",{"text":76,"@type":77},"The study aims to construct a risk factor prediction model for PROM using machine learning and to examine how PROM relates to the nutritional inflammatory index.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"How were predictive variables selected and models evaluated?",{"text":81,"@type":77},"Variables were screened using ridge regression and Boruta, then incorporated into multiple machine learning models. Model selection and evaluation used ROC AUC and additional metrics such as sensitivity, specificity, recall, F1 score, and accuracy.",{"name":83,"@type":74,"acceptedAnswer":84},"Which factors were identified as key predictors of PROM?",{"text":85,"@type":77},"The study reports key predictive factors including BMI, PNI, platelet, albumin, and AISI, with model performance assessed through ROC characteristics.","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":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":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"]