Ensemble learning for heterogeneous biomarker discovery in precision dairy farming

Metabolic biomarkers can act as powerful indicators of dairy cow welfare. Machine learning methods have been applied to discover discriminative biomarkers that can help predict potential risks to cow health, and consequently support early interventions to avoid decline in animal welfare and production loss. Previous studies on predictive models based on metabolic profiling have considered a limited scope of biomarkers, and have mostly been restricted to prediction of disease occurrence. This work proposes an ensemble supervised learning approach based on heterogeneous biomarker attributes obtained from dairy cow profile and history, and two metabolic profiling methods, to predict potential risks for dairy cow health, reproduction performance, and milk production loss. Best performing models are composed of either Random Forest, Multilayer Perceptropn and Extra Trees classifiers, and achieved F1 scores of 0.81, 0.86, 0.98 and 0.75 when predicting ‘Non-diseased’, ‘Bad’ or ‘Good’ reproduction performance, and ‘0’ production loss for an upcoming lactation. The source code and sample datasets for this work are made publicly available at https://github.com/bioinfoUQAM/dairy_biomarkers.