Artificial neural network prediction of ascites in broilers.
An artificial neural network was trained to predict the presence or absence of ascites in broiler chickens. The neural network was a three-layer back-propagation neural network with an input layer of 15 neurons (defining 15 physiological variables), a hidden layer of 16 neurons, and an output layer of 2 neurons (the presence or absence of ascites). Male by-products of a breeder pullet line were brooded at 32 and 30 C during Weeks 1 and 2, respectively. The training set for the neural network consisted of data from birds subjected to cool temperatures (18 C) to induce ascites. After training, the predictive ability of the neural network was verified with two new data sets. The second data set was from birds subjected to cool temperatures (18 C). The third data set was from birds subjected to clamping of the pulmonary artery to simulate the physiological processes involved in ascites (the temperature was 24 C). A comparison was made between laboratory diagnostic results and the neural network predicted ascites incidence. The neural network accurately identified the presence or absence of ascites in the first (training) set. Two false positives and one false positive were identified in the second and third verification sets, respectively. The birds identified as false positives were determined to be in the developmental stages of ascites before the occurrence of fluid accumulation. Artificial neural networks were found to effectively identify broilers with and without ascites.
Roush
W B
WB
Department of Poultry Science, Pennsylvania State University, University Park 16802, USA.
Kirby
Y K
YK
Cravener
T L
TL
Wideman
R F
RF
D000818:Animals; D001201:Ascites / Q000175:diagnosis / Q000453:epidemiology / Q000662:veterinary*; D002645:Chickens; D019411:Clinical Laboratory Techniques / Q000662:veterinary; D005189:False Positive Reactions; D015994:Incidence; D008297:Male; D016571:Neural Networks, Computer; D011201:Poultry Diseases; D011237:Predictive Value of Tests