Interpretable machine learning analysis for relationships between Helicobacter pylori infection and peripheral atherosclerosis: a retrospective cohort study

Background Helicobacter pylori ( H.pylori ) has been implicated in peripheral atherosclerosis (PA); however, its predictive value for PA risk in large population-based cohorts remains insufficiently characterized. Objectives This study aimed to evaluate assess the predictive contribution of H.pylori infection to PA risk, in combination with traditional clinical factors, using interpretable machine learning (ML) models. Methods A retrospective cohort of 5,862 individuals undergoing routine health check-ups was analyzed. Demographic data, laboratory indices, and lower-extremity vascular ultrasound findings were collected to determine PA status and H.pylori infection. Key risk factors were identified through univariate and multivariate logistic regressions. Subgroup and restricted cubic spline (RCS) analyses were applied to evaluate effect modification and nonlinear associations. Fourteen ML algorithms were developed and evaluated using the area-under-curve (AUC), sensitivity, accuracy, specificity, positive-predictive-value (PPV), negative-predictive-value (NPV), F1-score, Youden’s index, and calibration. Models were subsequently retrained using regression-identified variables to assess stability. SHapley Additive exPlanations (SHAP) analysis was employed to interpret feature importance from across models. Longitudinal and mediation analyses were conducted to explore temporal relationships and the potential mediating role of the triglyceride-glucose (TyG) index. Results H.pylori infection was independently associated with PA (odds ratio (OR)=5.27, 95%CI=4.27-6.54, P