Animal Disease Prediction Using Machine Learning

Abstract

Animal disease prediction is essential for ensuring livestock health, safeguarding public safety, and promoting sustainable agriculture. This study presents a Machine Learning (ML)-based framework that integrates diverse data sources, including environmental conditions, animal behavior, genetic information, and historical disease records, to forecast disease outbreaks and evaluate susceptibility. The framework employs data preprocessing, feature selection, and real-time monitoring to enhance prediction accuracy. By enabling proactive interventions, the system supports veterinarians and policymakers in making timely and informed decisions. Ultimately, this approach seeks to improve animal welfare, minimize economic losses, and strengthen the overall efficiency of disease management.