Quality-Risk Prediction of Veterinary Drugs by Data Mining
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Abstract:
Veterinary drug residues had become one of the source problems for food security at present. It was difficult for farmers to identify the fake veterinary drugs, which resulted in the risks of veterinary drugs quality. To improve the identification ability of the farmers and reduce the utilization of unqualified veterinary drugs, the data mining classification prediction model, established by C5.0, Logistic, neural network, was used to classify and predict the quality of veterinary drugs by sorting the sampling data of Chinese Veterinary Drug Administration based on SPSS Modeler software. Results showed that the classification accuracy of the three models was low, which resulted in optimizing the model by combination of classifier, and the neural network, binary logic regression - neural network, decision tree-neural network were compared. The overall performance of decision tree - neural network was the best in classification accuracy and generalization performance. Finally, the model for predicting the veterinary drugs quality in decision tree-neural network was established and further optimized, and the prediction accuracy reached 74.34%..
