Improving bovine disease detection through multilabel classification

Abstract

Abstract R1.C1: The dairy industry is a cornerstone of global food production and economic development; yet, its productivity is frequently hindered by common bovine health issues, including lameness, mastitis, metritis, and foot-and-mouth disease. These conditions not only affect milk yield but also pose significant challenges to maintaining animal welfare, highlighting the urgent need for intelligent, data-driven monitoring systems. R1.C2: In response to this critical need, this research proposes a machine learning (ML)-based framework for the early detection of such bovine events and diseases through multi-label classification. R1.C3: The system identifies estrus, calving, lameness, mastitis, and acidosis by analyzing key behavioral metrics derived from sensor-based monitoring, including feeding duration, resting periods, locomotion patterns, and aggregated activity data. R1.C4: In the context of multi-label bovine disease prediction, the combination of SMOTE and Classifier Chains is particularly crucial and synergistic due to the nature of the data and the interdependent relationships among the labels. R1.C5: The system was tested using a large dataset of 2.35 million records of livestock behavioral metrics. R1.C6: Among the six machine learning models investigated, the classifier chain configuration utilizing an Extra Tree Classifier consistently demonstrated superior performance, achieving a remarkable 97% subset accuracy, 96% recall, 95% precision, 96% F1-score, and a minimal Hamming loss of 0.04. Therefore, it is evident that classifier chains combined with oversampling techniques can capture label correlations and improve prediction performance compared to standard binary relevance approaches.