Development an artificial neural network to predict infectious bronchitis virus infection in laying hen flocks

초록

A three-layer, feed-forward artificial neural network (AN) with sixteen input neurons, three hidden neurons,and one output neuron was developed to identify the presence of infectious bronchitis (IB) infection as early as posiblein laying hen flocks. Retrospective data from flocks that enrolled IB surveillance program between May 2003 andNovember 2005 were used to build the ANN. Data set of 86 flocks was divided randomly into two sets: 77 casesfor training set and 9 cases for testing set. Input factors were 16 epidemiological findings including characteristicsof the layer house, management practice, flock size, and the output was either presence or absence of IB. AN wasto predict outcomes that it has never sen. Diagnostic performance of the trained network was evaluated by constructingreceiver operating characteristic (ROC) curve with the area under the curve (AUC), which were also used to determinethe best positivity criterion for the model. Several diferent ANNs with diferent structures were created. The best-fitted trained network, IBV_D1, was able to predict IB in 73 cases out of 77 (diagnostic accuracy 94.8%) in the training3, 95% CI, 79.8-99.3), respectively. For testing set, AUC of the ROC curve for the IBV_D1 network was 0.948(SE=0.086, 95% CI 0.592-0.961) in recognizing IB infection status accurately. At a criterion of 0.7149, the diagnosticaccuracy was the highest with a 88.9% with the highest sensitivity of 100%. With this value of sensitivity and specificitytogether with assumed 44% of IB prevalence, IBV_D1 network showed a PPV of 80% and an NPV of 100%. Basedon these findings, the authors conclude that neural network can be successfully applied to the development of a screeningmodel for identifying IB infection in laying hen flocks.

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