Evaluation of predictive models to determine final outcome for feedlot cattle based on information available at first treatment for bovine respiratory disease

OBJECTIVE To evaluate predictive model ability to determine whether an animal finished the feeding period using data known at first treatment for bovine respiratory disease (BRD). Additional comparisons evaluated the potential benefits of predictions by adding weather data, utilizing balancing techniques, and creating models for individual feedyards. ANIMALS This retrospective study included animal, pen, and feedyard data from 12 US feedyards from 2016 to 2021. The final dataset consisted of 96,382 BRD cases of which 14.2% did not finish the feeding phase. PROCEDURES Five predictive models were trained and underwent threshold probability adjustment to maximize F1 score. Model performance was evaluated using accuracy, sensitivity specificity, positive and negative predictive values, and area under the receiver operating characteristics curve (AUC). RESULTS Overall, model performance was low with a median AUC value of 0.675. The addition of weather data had little effect on AUC but resulted in more variation in sensitivity and specificity. Resampling the dataset had a limited effect on performance. Individual feedlot models had higher AUC values than others with the decision tree typically performing best in most feedyards. CLINICAL RELEVANCE Results indicated some utility of predictive models evaluating BRD cases to predict cattle that did not finish the feeding phase. These models could be valuable in assisting health providers making decisions on individual cases.