Colic is a leading cause of mortality in horses, demanding precise and timely interventions. This study integrates machine learning and explainable artificial intelligence (XAI) to predict survival outcomes in horses with colic, using clinical, procedural, and diagnostic data. Random forest and XGBoost emerged as top-performing models, achieving F1 scores of 85.9% and 86.1%, respectively. SHAP (Shapley additive explanations) was employed to provide interpretable insights, offering both global and local explanations for model predictions. The analysis revealed that key features, such as pulse rate, lesion type, and total protein levels, significantly influenced survival likelihood. Local interpretations highlighted the unique contribution of clinical factors to individual cases, enabling personalized insights that guide targeted treatment strategies. These tailored predictions empower veterinarians to prioritize interventions based on the specific conditions of each horse, moving beyond generalized care protocols. By combining predictive accuracy with interpretability, this study advances precision veterinary medicine, enhancing outcomes for equine colic cases and setting a benchmark for future applications of AI in animal health.

This study examines H1N1 and seasonal flu vaccination behaviors using machine learning models and explainable artificial intelligence (XAI) techniques. Utilizing data from the National 2009 H1N1 Influenza Survey, we developed a predictive framework employing models such as CatBoost, XGBoost, and LightGBM. CatBoost outperformed others with an accuracy of 0.696 and an F1 score of 0.688. SHAP (Shapley Additive Explanations) was used for interpretability, providing both global insights, such as the critical role of doctor recommendations, and local insights, highlighting individual decision factors. Our findings underscore the importance of addressing vaccine skepticism and improving healthcare communication to enhance vaccination uptake. These results contribute to public health strategies aimed at increasing immunization coverage and preparing for future pandemics.