Search for: All records

The objective of this study was to evaluate the performance of an XGBoost model trained with behavioral, physiological, performance, environmental, and cow feature data for classifying cow health status (HS). The model predicted HS based on physical activity, resting, reticulo-rumen temperature, rumination and eating behavior, milk yield, conductivity and components, temperature and humidity index, parity, calving features, and stocking density. Daily at 5 a.m., the model generated a HS prediction [0 = no health disorder (HD); 1 = health disorder]. At 7 a.m., technicians blind to the prediction conducted clinical exams on cows from 3 to 11 DIM to classify cows (n = 625) as affected (HD = 1) or not (HD = 0) by metritis, mastitis, ketosis, indigestion, displaced abomasum, and pneumonia. Using each day a cow presented clinical signs of HD as a positive case (i.e., HD = 1), metrics of performance (%; 95% CI) were: sensitivity (Se) = 57 [52, 62], specificity = 81 [80, 82]; positive predictive value (PPV) = 20 [18, 22], negative predictive value = 96 [95, 96], accuracy = 79 [78, 80], balanced accuracy = 69 [66, 72], F-1 Score = 29 [26, 32]. Sensitivity was also evaluated using fixed time intervals around clinical diagnosis of disease as a positive case (Table 1). Our findings suggest that the ability of an XGBoost algorithm trained on diverse sensor and nonsensor data to identify cows with HD was moderate when only days when cows presented clinical signs of disease were considered a positive case. Sensitivity and PPV can be improved substantially when all days within fixed intervals before and after clinical diagnosis are used as positive cases. Table 1 (Abstr. 2614). Sensitivity and PPV for an XGBoost algorithm trained to predict cow health status using fixed intervals before and after clinical diagnosis as positive cases Day relative to CD Se (%) 95% CI PPV (%) 95% CI −5 to 0 58 49, 67 21 16, 25 −3 to 0 55 46, 64 19 15, 24 −5 to 1 69 61, 78 24 20, 29 −5 to 3 81 73, 88 28 23, 33 −5 to 5 86 80, 92 30 25, 34 −3 to 1 67 58, 75 23 18, 27 −3 to 3 78 70, 86 27 22, 31 −3 to 5 83 76, 90 28 24, 33 0 to 3 75 68, 83 24 20, 29 0 to 5 81 73, 88 26 21, 31 −1 to 0 54 44, 63 18 14, 22 0 to 1 63 54, 72 20 16, 25 −1 to 1 66 57, 75 21 17, 26