Machine learning analysis of student consumer choices for pet supplements

Background Motivated by the rising global trend of veterinary Complementary and Alternative Medicine (CAM) usage and a specific data gap in Taiwan, this study investigates the consumption behavior of future pet owners. Methods A cross-sectional survey was conducted among Taiwanese medical university students using a validated online questionnaire. Beyond traditional descriptive statistics, this study employed machine learning techniques to analyze owner demographics, pet characteristics, and determinants of CAM usage. Results Data analysis revealed a strong correlation between positive owner perceptions—specifically satisfaction, belief in benefits, and understanding—and targeted CAM application. A decision tree model successfully identified “overall satisfaction” as the primary splitting criterion for user segmentation, followed by belief and understanding. Predictive modeling demonstrated high accuracy in identifying usage motivations for joint and digestive health, though predicting “immune system boosting” proved more complex due to behavioral variability. Conclusion Owner satisfaction is the critical predictor of CAM usage patterns. While the predictive model for specific conditions, such as joint and digestive health, yielded high accuracy (AUC > 0.93), these findings should be interpreted as an exploratory framework given the pilot nature of the study and the limited sample size ( n = 41). These findings suggest that veterinarians and industry stakeholders should adopt data-driven communication strategies focusing on transparency and satisfaction.