Multivariate Analysis and Logistic Regression for Identification of Gut-brain Axis Disorders Risk Factors: A Comparative Study with Machine Learning Methods (#949)

Authors

Flores, Ethel

Ponce, Jovita

Cardona, Ana

Gonzales, Rene

Funez, Victor

Oliva, Karen

Urmeneta, Jorge

Abstract

Gut-brain axis disorders (GBAD) are a major public health issue in developing nations, especially Central America, where limited healthcare resources require effective disease detection and prevention. This study compares classic statistical methods and modern machine learning algorithms for identifying GBAD risk variables in Hondurans. A dataset of 1,847 12-49-year-olds was constructed using Monte Carlo simulation and epidemiological factors from regional health surveys. We used Random Forest, Gradient Boosting, Support Vector Machine, and Multilayer Perceptron Neural Network classifiers with multivariate logistic regression, chi-square analysis, and odds ratio estimates. Ten-fold stratified cross-validation calculated AUC-ROC, sensitivity, specificity, and F1-score. We found that multivariate logistic regression outperformed machine learning methods in discrimination (AUC-ROC = 0.692, 95% CI: 0.649-0.735), with Random Forest having the highest AUC (0.609). Female sex, smoking, alcohol consumption, and previous gastrointestinal diagnosis were risk factors, but physical activity was protective (OR = 0.723, 95% CI: 0.575-0.908). Traditional statistical methods may outperform advanced machine learning models in epidemiological studies with moderate sample sizes and interpretability criteria, yielding clinically useful risk estimates for primary healthcare interventions