Animal Disease Prediction Using Machine Learning

Animal diseases significantly affect livestock productivity, food security, and the economic stability of farmers. Early identification of diseases in animals is often difficult because many symptoms appear similar across different illnesses and farmers may lack access to timely veterinary support. This research proposes a machine learning based approach for predicting animal diseases using clinical symptoms and environmental information. The system analyzes data such as body temperature, appetite condition, respiratory symptoms, and behavioral patterns to identify possible diseases at an early stage. Several machine learning algorithms including Decision Tree, Random Forest, and Support Vector Machine are trained and evaluated using a labeled dataset of animal health records. Data preprocessing techniques such as cleaning, normalization, and feature selection are applied to improve model performance. The results show that machine learning models can effectively classify animal diseases with high prediction accuracy and provide quick decision support for farmers and veterinarians. The proposed model demonstrates the potential of intelligent systems in improving livestock health management and reducing economic losses caused by delayed diagnosis.