Exploring machine learning algorithms for early prediction of clinical mastitis

Different classification machine learning techniques (Naïve Bayes, Random Forest and Extreme Gradient Boosting) were evaluated to identify cows positive for clinical mastitis (CM) during their first lactation (1st lactation) daily predict the onset of CM (continuous). Integrated data from different software used feed algorithms. In both cases, best predictions obtained with algorithm. The algorithms correctly classified 71% 85% 1st continuous models, respectively. Both analyses had same accuracy 72%. Results showed that it is feasible integrate streams develop predictive prescriptive decision support tools. Having two working concomitantly, one predicting imminent risk other overall lactation, could help in short, mid-, long-term decision-making process.