Abstract:

Animal health is integral to food security, rural livelihoods, and economic sustainability, particularly in developing nations like India where cattle form the backbone of the livestock sector. However, traditional methods of diagnosing bovine diseases are often time-consuming, resource-intensive, and inaccessible to small-scale farmers. This paper proposes a data-driven approach using machine learning (ML) models for the early prediction and classification of cattle diseases based on physiological and environmental indicators. A structured preprocessing pipeline was applied to a numerical dataset capturing features such as body temperature, heart rate, saliva pH, and more. Multiple classifiers including Linear Discriminant Analysis (LDA), Gaussian Naïve Bayes, Decision Trees, K-Nearest Neighbors (KNN), Linear SVM, and Logistic Regression were evaluated on accuracy, log-loss, and class-wise performance metrics. Results indicate that probabilistic models such as LDA and Gaussian Naïve Bayes outperform others, achieving high accuracy (>98%) and robust generalization across disease types. The study demonstrates the feasibility and effectiveness of intelligent disease prediction systems in livestock health monitoring and provides insights into the most reliable ML models for real-world deployment.