Conference Agenda

Gut-brain Axis Disorders (GBAD) afflict 15-20% of Hondurans, making them a major public health issue. At-risk people can be identified early to improve Primary Health Care (PHC) prevention. Objective: This work developed and validated machine learning models to predict GBAD presence using sociodemographic, behavioral, and environmental characteristics from standardized PHC surveys. Methods: We generated 1,200 synthetic patient records using epidemiological parameters from the literature and a validated survey instrument for communities on the Public Health IV rotation at the Universidad Nacional Autonoma de Honduras (UNAH). The following classification techniques were tested: Random Forest (RF), Support Vector Machine (SVM), Gradient Boosting (GB), and Multilayer Perceptron Neural Network. All algorithms were analyzed for feature relevance and model performance using 10-fold stratified cross-validation. Results: The simulated dataset had 27.5% GBAD prevalence, matching regional estimates. Random Forest performed best in cross-validation (AUC = 0.594 ± 0.067), followed by Gradient Boosting (AUC = 0.591 ± 0.068). Cross-model feature importance analysis found age, perceived stress, fiber consumption, and education as the most influential predictors. Conclusions: Machine learning has moderate potential for PHC GBAD screening. The identified risk factors support epidemiological research and give prevention program targets. Integrating these models into PHC operations could aid early detection and intervention.