Minimal Number of Chicken Daily Growth Velocities for Artificial Neural Network Detection of Pulmonary Hypertension Syndrome (PHS)

Abstract

Previously, evaluation of the first 2 wk of daily growth velocity with an artificial neural network (ANN) provided an effective noninvasive approach for predicting the susceptibility of broilers to pulmonary hypertension syndrome (PHS). This study was conducted to define the minimum number of days of growth data and the type of ANN required for the best prediction of PHS susceptibility. Four experiments were conducted in which broilers were weighed daily at 0800 h. In Experiment 1, Hubbard male broilers were reared to 50 d of age, with 13 developing PHS and 33 remaining normal (N), for a PHS:N ratio of 13:33. In Experiment 2, ANAK broilers were exposed to cool temperatures (16 to 17 C) from 17 to 42 d of age, resulting in a PHS:N ratio of 16:46 for males. In Experiments 3 and 4, Hubbard male and female chicks from a base population and a PHS-resistant line were exposed to cool temperatures from 17 to 42 d (Experiment 3) or 49 d of age (Experiment 4). The PHS:N ratios were 40:68 for males and 6:96 for females in Experiment 3 and 26:91 for males and 10:58 for females in Experiment 4. Four ANN, back propagation (BP3), Ward back propagation (WardBP), probabilistic (PNN), and general regression (GRNN), were evaluated for their ability to predict PHS in the shortest number of days based on daily growth velocities (BWd+1-BWd). A 100% prediction of PHS and N birds was considered the criterion of success. Starting with 14 d of data, each ANN was trained on daily growth velocity, and the number of predictive days was reduced with each run of the ANN. The best ANN was a GRNN, which correctly diagnosed PHS and N male broilers on 4 and 6 d of growth velocity data for Experiments 1 and 2, respectively. The results were poorer with the BP3, WardBP, and PNN. The diagnostic ability of the neural network was not consistent over all four experiments. In Experiment 2, a minimum of 6 d was required for 100% PHS detection for males. In Experiment 3, the best diagnostic value for males was 93% PHS detection and 100% N detection at 15 d. For females, the 100% PHS detection occurred at a minimum of 8 d. In Experiment 4, males had 100% PHS and N detection at a minimum of 11 d. Females had a 100% PHS and N detection at a minimum of 10 d. An attempt to build a single neural network that would detect PHS susceptibility in Hubbard (Experiment 1) and ANAK (Experiment 2) broilers was unsuccessful. The application (validation) of neural networks between experiments also was not successful (data not presented). However, these studies demonstrate that within a breed or line reared under similar selection pressures for ascites, a GRNN based on the first 14 d of growth velocity can detect, with at least 93% accuracy, broilers susceptible to PHS.