A machine learning based decision aid for lameness in dairy herds using farm-based records

Abstract

Lameness is a common welfare issue in dairy farms and can negatively influence productivity and farm profitability. On-farm measurements are time consuming and continuous monitoring of lameness can be thus challenging. A predictive model that is suitable for routine field applications can be thus an efficient strategy to improve lameness status in dairy herds. We explored the use of a machine learning approach based on decision tree induction to detect and monitor the risk level of dairy herds for lameness based on 20 routinely pre-collected farm-based records related to herd management and housing, milk production performance, reproduction performance, longevity, and genetic merit. The risk of lameness was determined for 229 dairy herds based on herd prevalence. The results based on 10-fold repeated cross-validation suggest that there was little difference between a simple decision tree and more advanced machine learning algorithms such as random forests or boosting algorithms with an area under the receiver operating characteristic curve (AUC) of 0.73–0.75, but machine learning performed better than classical multivariate logistic regression (AUC of 0.28). Model sensitivity based on machine learning was highest at 0.58, whereas model specificity was highest at 0.89 among all models tested. Stacking the individual machine learning algorithms to a higher-order model slightly lifted model performance (AUC of 0.76) at a model sensitivity of 0.54 and a model specificity of 0.94 but at a loss in model interpretability. Additional information more closely related to lameness are required to further improve model performance. Nevertheless, decision tree induction was found to be useful in analysing small data sets that frequently occur in various livestock farming disciplines. Overall, the machine learning approach described in our study may be an appropriate and powerful decision support and monitoring tool, and can be implemented in a computerized information system to detect herds with potential deficiencies in lameness.