Biomarker-based learning for disease prediction in precision dairy farming
Authors: H. Almeida and N. B. Grégoire and M. Bilal and M. Leduc and Y. Chorif and X. Zhao and J. Dubuc and A. B. Diallo
Date: 2024-07-01
Status: Published
Metabolic diseases have great impact on dairy production and animal welfare [1, 2]. Metabolomic profiling has helped identify biomarkers to predict disease risk in dairy cows [3, 4]. Previous studies tend to overlook other biomarkers, like from milk production, which could help predict diseases in cows [5]. Our ensemble learner supports predicting disease risk based on heterogeneous biomarkers from metabolomic and health profiles, milk production history, and herd history.
Our datasets contain biomarkers for over 13,700 health events of 1,200 cows from 50 dairy farms in Canada. Biomarkers are captured for a health event e at timepoint t. Given an upcoming lactation Ln, base predictions are obtained for all health events et occurring during lactation Ln−1.
The ensemble learner averages base predictions for an animal and outputs disease probabilities for lactation Ln. Binary classes are disease or non-disease, based on a curated set of nine most common diseases in dairy cows. Classification performance was evaluated for multiple combinations of biomarkers and classifiers.
Classification models based on Logistic Regression and Random Forest classifiers yield best performances, with an average of 0.6 and 0.77 F-measure for disease and non-disease respectively.