Supervised machine learning-based prediction of bovine anaplasmosis in Florida beef cattle: A 10-year retrospective study

The lack of updated information on the prevalence and risk fac­tors of bovine anaplasmosis (BA), across the United States poses challenges in accurately assessing production losses and devel­oping effective prevention and control strategies. Supervised machine learning (SML) algorithms are potentially powerful tools that may be used for identifying risk factors associated with infectious diseases such as BA. Here, we compared 6 dif­ferent SML models to predict Anaplasma marginale seropositiv­ity in Florida beef cattle.