Machine Learning for Predicting Somatic Cell Count in Holstein Cattle
This study demonstrates that nonlinear machine learning models, specifically Bayesian Neural Networks and Random Forests, outperform conventional linear and support vector methods in predicting natural log-transformed somatic cell counts in Holstein cattle using routine farm data, although predictive accuracy declines in cows with more than four lactations.