Supervised Machine Learning Approaches for Early Detection of Metabolic and Udder Health Disorders in Dairy Cows Using Sensor-Derived Data

Akvilė Girdauskaitė^1,2*Samanta Grigė²Karina Džermeikaitė²Justina Krištolaitytė²Dovilė Malašauskienė²Mindaugas Televičius²Greta Šertvytytė²Gabija Lembovičiūtė²Ramūnas Antanaitis²

• ¹Coordinating Center for Rare and Undiagnosed Diseases, Hospital of Lithuanian University of Health Sciences Kaunas Clinics, Kaunas, Lithuania
• ²Lietuvos sveikatos mokslu universitetas Veterinarijos akademija, Kaunas, Lithuania

This study assessed five supervised machine learning (ML) models. Automated devices that continuously captured milk composition and behavioral data were used to monitor 206 Holstein cows from two commercial dairy farms. Milk yield, fat, protein, lactose, fat-to-protein ratio (FPR), somatic cell count (SCC), rumination time (RT), and body temperature were among the parameters that were noted. Cows were categorized as clinically healthy (n = 45), subclinical ketosis (n = 91), subclinical mastitis (n = 28), or clinical mastitis (n = 42) based on clinical examination, blood β-hydroxybutyrate (BHB) concentration, and milk indicators. Random Forest achieved the highest classification accuracy (0.857), followed by Gradient Boosting and Logistic Regression (0.833), while Decision Tree and Multilayer Perceptron reached 0.810. Compared to clinically healthy cows (4.45 ± 0.54%; 477.0 ± 36.0 min/day), subclinical ketosis cows had a greater milk fat content (5.21 ± 0.72%) and a shorter RT (336.9 ± 94.2 min/day). In comparison to clinically healthy cows (64.0 × 10³ cells/mL; 4.63 ± 0.16%), cows with clinical mastitis showed significantly greater SCC (416.8 × 10³ cells/mL) and lower lactose levels (4.56 ± 0.24%). These results demonstrate that integrating sensor-derived milk and behavioral data with ML algorithms enables early, non-invasive detection of health disorders, supporting proactive herd management.