Comparative Analysis: Machine Learning vs. Artificial Neural Networks for Animal Disease Prediction

Accurate animal disease prediction is essential for improving livestock health and mitigating the impact of disease outbreaks, thereby safeguarding food security andreducing agricultural losses. This study presents a comparative analysis of two prominent approaches: Machine Learning (ML) and Artificial Neural Networks (ANN), for predicting animal diseases using symptoms and temperature data. Our experiments revealed that while both methods offer valuable insights, the ANN model, implemented with TensorFlow and Keras, outperforms ML in predictive accuracy. The Support Vector Classifier (SVC) achieved an accuracy of 79.59%, whereas the ANN model demonstrated superior accuracy and robustness, making it a more reliable tool for early disease detection and livestock health management.