ORIGINAL RESEARCH article
Front. Med.
Sec. Family Medicine and Primary Care
Volume 12 - 2025 | doi: 10.3389/fmed.2025.1587540
This article is part of the Research TopicAI with Insight: Explainable Approaches to Mental Health Screening and Diagnostic Tools in HealthcareView all 6 articles
Machine learning for prediction of Helicobacter pylori infection based on Basic health examination data in adults: a retrospective study
Provisionally accepted
- People’s Hospital of Deyang City, Deyang, China
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Objective: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.Methods ： 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 feature selection. 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.Results：A total of 10,393 subjects were included in the dataset, w ith 3,278 (31.54%) having H pylori infection. After feature screenin g, 10 factors were selected for the prediction model.Among six m achine -learning models, the Extra Trees model had the best perf ormance, 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 predic tive capability.SHAP analysis showed that age, WBC, ALB, gender, a nd wasit were the top five factors affecting H pylori infection. Hig her age, WBC,wasit and lower ALB were linked to a higher infecti on probability. These results offer insights into H pylori infection ri sk factors and model performance.Conclusions： 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.
Keywords: machine learning, H pylori infection, Basic health examination, SHAP analysis, Health examination
Received: 04 Mar 2025; Accepted: 27 May 2025.
Copyright: © 2025 Wang, Liang, Li, Liu and Zhou. 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) or licensor 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.
* Correspondence: Xiaoqing Liu, People’s Hospital of Deyang City, Deyang, China
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