[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125283-en":3,"doc-seo-125283-105":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},125283,2336464648746,"Skyler","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults - a retrospective study","This retrospective study evaluates machine learning models to predict Helicobacter pylori infection in adults using routinely collected health screening data, combining demographic characteristics with clinical biomarkers. Feature selection is performed using LASSO regression, while six distinct algorithms are trained and compared through comprehensive performance evaluation. SHAP is applied to visualize model drivers at both the feature and individual-case levels. Extra Trees achieves the strongest overall performance, supported by SHAP-identified key risk factors.","TYPE Original Research PUBLISHED 13 June 2025  \nDOI 10.3389/fmed.2025.1587540  \nOPEN ACCESS  \nEDITED BY  \nInbar Levkovich,  \nTel-Hai College, Israel  \nREVIEWED BY  \nTürkan Mutlu Yar, Ordu University, Türkiye Yeliz Kasko Arici,  \nOrdu University, Türkiye  \n*CORRESPONDENCE  \nXiaoqin Liu  \n [66823572@qq.com](66823572@qq.com)  \n†These authors have contributed equally to this work and share first authorship  \nRECEIVED 04 May 2025  \nACCEPTED 27 May 2025  \nPUBLISHED 13 June 2025  \nCITATION  \nWang Q, Liang T, Li Y, Zhou P and Liu X (2025) Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults:  \na retrospective study.  \nFront. Med. 12:1587540 .  \ndoi: 10.3389/fmed.2025.1587540  \nCOPYRIGHT  \n© 2025 Wang, Liang, Li, Zhou and Liu. This isan 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.  \nMachine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults: a retrospective study  \nQiaoli Wang1†, Tao Liang2†, Yuexi Li1 , Peng Zhou1 and Xiaoqin Liu1*  \n1 Health Management Center, Deyang People’s Hospital, Deyang, Sichuan, China, 2 Department of Gastroenterology, Deyang People’s Hospital, Deyang, Sichuan, China  \nObjective: This study aimed to investigate the feasibility of developing machine learning models for non-invasive prediction of Helicobacter pylori (H pylori) infection using routinely collected adult health screening data, including demographic characteristics and clinical biomarkers, to establish a potential decision-support tool for clinical practice.  \nMethods: The data was sourced from the adult health examination records within the health management centers of the hospital. The Least Absolute Shrinkage and Selection Operator (LASSO) regression was employed for featureselection. Six distinct machine learning algorithms were utilized to construct the predictive models, and their performance was comprehensively evaluated. Additionally, the SHapley Additive Projection (SHAP) method was adopted to visualize the model features and the prediction results of individual cases.  \nResults: A total of 10,393 subjects were included in the dataset, with 3,278 (31 . 54%) having H pylori infection. After feature screening, 10 factors were selected for the prediction model. Among six machine—learning models, the Extra Trees model had the best performance, with an AUC of 0.827, Accuracy of 0 .744, and Recall of 0 .736. The Random Forest model also did well, with an AUC of 0 .810. XGBoost attained an AUC of 0 . 801, indicating moderate predictive capability. SHAP analysis showed that age, WBC, ALB, gender, and wasit were the top five factors affecting H pylori infection. Higher age, WBC, wasit and lower ALB were linked to a higher infection probability. These results offer insights into H pylori infection risk factors and model performance.  \nConclusion: The Extra Trees classifier exhibited the optimal performance in predicting H pylori infections among the evaluated models. Additionally, the SHAP analysis enhanced the interpretability of the model, which offers valuable insights for early—stage clinical prediction and intervention strategies.  \nKEYWORDS  \nmachine learning, H pylori infection, basic health examination, SHAP analysis, health examination  \nFrontiers in Medicine 01 [frontiersin.org](frontiersin.org)  \nIntroduction  \nH pylori, a gram􀂗negative bacterium, has the remarkable ability to survive in the harsh acidic environment of the human stomach and colonize the epithelial cells of the gastric mucosa. Globally, H pylori is an extremely prevalent patho","cbCailNqAOh8Jnvo","https://ap.wps.com/l/cbCailNqAOh8Jnvo","pdf",5044160,1,13,"English","en",105,"# Objective\n# Methods\n## Feature selection\n## Model training and evaluation\n## SHAP interpretability\n# Results\n# Conclusion","[{\"question\":\"What is the study’s main objective?\",\"answer\":\"To assess the feasibility of building machine learning models that non-invasively predict Helicobacter pylori infection using adult health screening data.\"},{\"question\":\"Which methods are used to build and interpret the predictive models?\",\"answer\":\"LASSO regression is used for feature selection, six machine learning algorithms are compared for prediction performance, and SHAP visualizes feature importance and individual prediction contributions.\"},{\"question\":\"How well do the models perform, and which model is best?\",\"answer\":\"Among six models, Extra Trees performs best with AUC 0.827, accuracy 0.744, and recall 0.736; Random Forest also shows strong performance with AUC 0.810.\"}]","Machine learning for prediction of Helicobacter pylori infection based on basic health examination data in adults - a retrospective study | PDF",1785897946,33,{"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},"machine-learning-for-prediction-of-helicobacter-pylori-infection-based-on-basic-health-examination-data-in-adults-a-retrospective-study","",{"@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/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/machine-learning-for-prediction-of-helicobacter-pylori-infection-based-on-basic-health-examination-data-in-adults-a-retrospective-study/125283/",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","2026-08-05",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 is the study’s main objective?","Question",{"text":75,"@type":76},"To assess the feasibility of building machine learning models that non-invasively predict Helicobacter pylori infection using adult health screening data.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which methods are used to build and interpret the predictive models?",{"text":80,"@type":76},"LASSO regression is used for feature selection, six machine learning algorithms are compared for prediction performance, and SHAP visualizes feature importance and individual prediction contributions.",{"name":82,"@type":73,"acceptedAnswer":83},"How well do the models perform, and which model is best?",{"text":84,"@type":76},"Among six models, Extra Trees performs best with AUC 0.827, accuracy 0.744, and recall 0.736; Random Forest also shows strong performance with AUC 0.810.","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},[93,97,101,105,110,115,120,123,128,131,135],{"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":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},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"]