Explainable Classification Models for Identifying Elevated Gastrointestinal Symptom Burden in Honduran Communities: A Cross-Sectional Study

Background/Objectives: Gastrointestinal symptoms are a common reason for primary-care consultation, yet specialist assessment is often inaccessible in rural Central America. We evaluated whether explainable models using inexpensive demographic and lifestyle data can identify community residents aged 12 to 49 years with concurrently elevated gastrointestinal symptom burden. Methods: This was a cross-sectional survey of 1838 residents from 85 communities in five Honduran departments. Elevated symptom burden was defined as at least two of four indicators: recent abdominal pain, heartburn, gastroesophageal reflux, and post-prandial bloating. Model A used eight demographic and lifestyle predictors; Model B added self-reported previous digestive diagnosis. Logistic regression was compared with six machine-learning classifiers under repeated nested five-fold and ten-fold cross-validation and community-grouped cross-validation. Discrimination, calibration, paired differences, community-level uncertainty, decision curves, SHAP explainability and subgroup performance were assessed. Results: Elevated symptom burden affected 840/1838 participants (45.7%, 95% CI 43.4 to 48.0). Model A showed modest discrimination (AUROC 0.663, 95% CI 0.639 to 0.688), while Model B improved performance (AUROC 0.726, 95% CI 0.702 to 0.748) with good calibration (slope 0.955). No flexible learner outperformed logistic regression; the paired AUROC difference with gradient boosting was 0.001 (95% CI −0.008 to 0.010). Within strata of previous diagnosis, discrimination was only 0.621 to 0.643. Model B provided net benefit above screen-all and screen-none strategies at thresholds above approximately 0.25, but reaching 80% sensitivity required flagging 66% of participants. Conclusions: Low-cost information can identify concurrently elevated gastrointestinal symptom burden with moderate accuracy. A parsimonious, well-calibrated logistic regression model performed comparably to more complex methods and is a reasonable candidate for community-based identification of current burden, not for forecasting risk. Prospective external validation and age-specific thresholds are required before clinical implementation.