Computer algorithms are routinely used to aid in the identification of biological patterns not easily detected with standard statistics. Currently, observed changes in normal patterns of feeding behavior (FB) are used to identify morbid feedlot cattle. The objective of this study was to use pattern classification techniques to develop algorithms capable of identifying morbid (M) cattle earlier than traditional pen checking methods. In two separate studies, individual feeding behaviour was obtained from 384 feedlot steers (228 ± 22.7 kg, initial BW) in a 226 d trial (model dataset), and 384 feedlot heifers (322 ± 34.7 kg, initial BW) in a 142 d trial (naive dataset). Data was collected using an automated feed bunk monitoring system. FB variables calculated included feeding duration, inter-meal interval (min., max., avg., SD and total; min/d) and feeding frequency (visits/d). Animal health records including the number of times treated, d in the hospital and d on feed were also collected. Ninety-three and 53 morbid (M) animals were identified in each trial respectively, and were categorized into low, moderate and high groups, based on severity of sickness. FB data for 68 cattle from the model dataset (45 classified as Moderate and 25 classified as High) was analyzed to develop an algorithm which would aid in identifying morbid FB. This algorithm was later tested on 18 M animals (12 classified as Moderate and 6 as High) in the naive dataset. The pattern recognition procedure involved reducing data dimensionality via Principal Component Analysis, followed by K-means clustering and finally the development of a binary string to aid in the classification of M feeding behaviour. The developed procedure resulted in an overall classification accuracy of 84 % (82.5 and 85 % accuracy for H and M, respectively) for the model dataset, and 75 % overall (100 and 50 % accuracy for H and M, respectively) for the naive dataset. The model predicted morbidity on average 3.3 and 1.2 d earlier than pen checkers could for each trial respectively. The application of pattern recognition algorithms to FB shows value as a method of identifying morbid cattle in advance of overt physical signs of morbidity.

Computer algorithms are routinely used to aid in the identification of biological patterns not easily detected with standard statistics. Currently, observed changes in normal patterns of feeding behavior (FB) are used to identify morbid feedlot cattle. The objective of this study was to use pattern classification techniques to develop algorithms capable of identifying morbid (M) cattle earlier than traditional pen checking methods. In two separate studies, individual feeding behaviour was obtained from 384 feedlot steers (228 ± 22.7 kg, initial BW) in a 226 d trial (model dataset), and 384 feedlot heifers (322 ± 34.7 kg, initial BW) in a 142 d trial (naive dataset). Data was collected using an automated feed bunk monitoring system. FB variables calculated included feeding duration, inter-meal interval (min., max., avg., SD and total; min/d) and feeding frequency (visits/d). Animal health records including the number of times treated, d in the hospital and d on feed were also collected. Ninety-three and 53 morbid (M) animals were identified in each trial respectively, and were categorized into low, moderate and high groups, based on severity of sickness. FB data for 68 cattle from the model dataset (45 classified as Moderate and 25 classified as High) was analyzed to develop an algorithm which would aid in identifying morbid FB. This algorithm was later tested on 18 M animals (12 classified as Moderate and 6 as High) in the naive dataset. The pattern recognition procedure involved reducing data dimensionality via Principal Component Analysis, followed by K-means clustering and finally the development of a binary string to aid in the classification of M feeding behaviour. The developed procedure resulted in an overall classification accuracy of 84 % (82.5 and 85 % accuracy for H and M, respectively) for the model dataset, and 75 % overall (100 and 50 % accuracy for H and M, respectively) for the naive dataset. The model predicted morbidity on average 3.3 and 1.2 d earlier than pen checkers could for each trial respectively. The application of pattern recognition algorithms to FB shows value as a method of identifying morbid cattle in advance of overt physical signs of morbidity.