Evaluating the efficacy of bioelectrical impedance analysis using machine learning models for the classification of goats exposed to Haemonchosis - Siddique, Aftab; Batchu, Phaneendra; Shaik, Arshad; Gurrapu, Priyanka; Erukulla, Tharun Tej; Ellington, Cornileus; Rubio Villa, Andrea L.; Brown, Davia; Mahapatra, Ajit; Panda, Sudhanshu; Morgan, Eric; Van Wyk, Jan; et al. (Frontiers in Veterinary Science, 2025)
Rapid identification and assessment of animal health are critical for livestock productivity, especially for small ruminants like goats, which are highly susceptible to blood-feeding gastrointestinal nematodes, such as Haemonchus contortus. This study aimed at establishing proof of concept for using bioelectrical impedance analysis (BIA) as a non-invasive diagnostic tool to classify animals at different levels of Haemonchosis. A cohort of 94 intact Spanish bucks (58 healthy; 36 Unhealthy; naturally infected with H. contortus) was selected to evaluate the efficacy of BIA through the measurement of resistance (Rs) and electrical reactance (Xc). Data were collected from live goats using the CQR 3.0 device over multiple time points. The study employed several machines learning models, including Support Vector Machines (SVM), Backpropagation Neural Networks (BPNN), k-Nearest Neighbors (K-NN), XGBoost, and Keras deep learning models to classify goats based on their bioelectrical properties. Among the classification models, SVM demonstrated the highest accuracy (95%) and F1-score (96%), while K-NN showed the lowest accuracy (90%). For regression tasks, BPNN outperformed other models, with a nearly perfect R2 value of 99.9% and a minimal Mean Squared Error (MSE) of 1.25e-04, followed by SVR with an R2 of 96.9%. The BIA data revealed significant differences in Rs and Xc between lightly and more heavily Unhealthy goats, with the latter exhibiting elevated resistance values, likely due to dehydration and tissue changes resulting from Haemonchosis. These findings highlight the potential of BIA combined with machine learning to develop a scalable, rapid, and non-invasive diagnostic tool for monitoring small ruminant health, particularly in detecting parasitic infections like H. contortus. This approach could improve herd management, reduce productivity losses, and enhance animal welfare.