A Machine Learning and Optimization Framework for the Early Diagnosis of Bovine Respiratory Disease

Abstract

Bovine Respiratory Disease (BRD) is an infection of the respiratory tract that is the leading reason for antimicrobial use in dairy calves and represents 22% of calf mortalities. The costs and effects of BRD can severely damage a farm's economy, since raising dairy calves is one of the largest economic investments, and diagnosing BRD requires intensive and specialized labor that is hard to find. Precision technologies based on the Internet of Things (IoT), such as automatic feeders, scales, and accelerometers, can help detect behavioral changes before outward clinical signs of BRD. Such early detection enables early treatment, and thus faster recovery, with less long term effects. In this paper, we propose a framework for BRD diagnosis, its early detection, and identification of BRD persistency status using precision IoT technologies. We adopt a machine learning model paired with a cost-sensitive feature selection problem called Cost Optimization Worth (COW). COW maximizes prediction accuracy given a budget constraint. We show that COW is NP-Hard, and propose an efficient heuristic with polynomial complexity called Cost-Aware Learning Feature (CALF). We validate our methodology on a real dataset collected from 159 calves during the preweaning period. Results show that our approach outperforms a recent state-of-the-art solution. Numerically, we achieve an accuracy of 88% for labeling sick and healthy calves, 70% of sick calves are predicted 4 days prior to diagnosis, and 80% of persistency status calves are detected within the first five days of sickness.