PubMed1997
A Probabilistic Neural Network (PNN) was trained to predict ascites in broilers based on minimally invasive inputs (i.e., physiological factors that do not require the death of the bird). A PNN is a supervised, three-layer, artificial neural network that classifies input patterns (e.g., physiological data) into specific output categories (e.g., ascites or no ascites). The PNN inputs were O2 level in the blood, body weight, electrocardiogram (ECG), hematocrit, S wave, and heart rate of individual birds. These data were from three experiments that have been described previously (Roush et al., 1996a,b). The three data sets were pooled into a combined data set for a total of 170 observations. From the pooled data, a training set (117 birds), a calibration set (17 birds), and a verification set (36 birds) were extracted. The PNN was trained on the training data set. To prevent the PNN from overfitting the training data, the neural network was evaluated on its ability to make correct predictions of the calibration data set. At the point at which the neural network made the highest number of correct classifications for the calibration data set, the trained neural network was saved on the computer. When the PNN was applied to the complete data set, the sensitivity or proportion of the birds with ascites that the PNN correctly diagnosed was 0.97 (75/77 birds). The specificity or proportion of birds that the PNN made a correct diagnosis of not having ascites was 0.98 (91/93 birds). When the PNN was applied to the verification data set, which was not subjected to neural network training, the sensitivity was 0.95 (19/20) and the specificity was 0.88 (14/16 birds). Use of models developed with artificial neural networks may enhance the diagnosis of ascites in broilers. The results may be useful in choosing and developing broiler strains that do not have a propensity for ascites.