Comparative Analysis of Machine-Learning and Deep-Learning Approaches for Accurate Animal Disease Prediction and Health Risk Assessment

Effective, efficient, and early animal disease prediction is a challenging task. Identifying and reducing animal health risks is important for preventing disease outbreaks and improving cattle management. This study presents the machine-learning and hybrid deep-learning models for animal risk prediction. We employed eight classifiers (Support Vector Machine, Logistic Regression, Decision Tree, K-Nearest Neighbors, Gaussian Naive Bayes, and Random Forest) along with feature-enhanced hybrid variants (RF–CNN and RF–ANN) to early detect risk to animals’ health. Our main objective is to develop and evaluate robust ML models for predicting animal health risks. Apart from these, we also present a comparative study of the conventional and hybrid models to construct a decision support system for early disease prediction. The experimental work reveals that RF obtained the highest accuracy of 95.77%, a macro F1-score of 0.9343, and a weighted F1-score of 0.9515. We also conduct the statistical test to confirm the robustness of the model for animal disease prediction. The proposed framework provides a scalable, interpretable decision-support system for real-world animal health monitoring and early disease intervention.