Probing Norwalk-like virus presence in shellfish, using artificial neural networks.
A database was examined using artificial neural network (ANN) models to investigate the efficacy of predicting PCR-identified Norwalk-like virus presence and absence in shellfish. The relative importance of variables in the model and the predictive power obtained by application of ANN modelling methods were compared with previously developed logistic regression models. In addition, two country-specific datasets were analysed separately with ANN models to determine if the relative importance of the input variables was similar for geographically diverse regions. The results of this analysis found that ANN models predicted Norwalk-like virus presence and absence in shellfish with equivalent, and better, precision than logistic regression models. For overall classification performance, ANN modelling had a rate of 93%, vs 75% for the logistic regression. ANN models were able to illuminate the site-specific relationships between indicators and pathogens.
Dept of Civil Engineering, University of Kentucky, Lexington, KY 40506, USA. gbrion@engr.uky.edu
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Brion
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Lingeriddy
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Neelakantan
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Wang
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Girones
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Lees
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Allard
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Vantarakis
D016208:Databases, Factual; D005506:Food Contamination; D005544:Forecasting; D016015:Logistic Models; D016571:Neural Networks, Computer; D029322:Norovirus; D016133:Polymerase Chain Reaction; D012758:Shellfish / Q000821:virology*