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Diagnosis of acute appendicitis in two databases. Evaluation of different neighborhoods with an LVQ neural network.

The use of an artificial neural network system was studied in the diagnosis of acute abdominal pain, especially acute appendicitis, with patients from Finland and Germany. Separate Learning Vector Quantization (LVQ) neural networks were trained with a training set from each database and also with a combined database. Each neural network was evaluated separately with a test set of cases from each database. With the combined database different neighborhood methods were compared to find the optimal choice for this decision-making problem. The acute appendicitis cases of the Finnish test data set were classified well with all the networks, but the cases of the German test set were difficult to classify for the Finnish network. The use of larger neighborhoods increased the sensitivity of the classification by nearly 10%. The differences in the results of the Finnish and German databases suggest that there are differences in the data collection or patient populations between centers. Therefore, care must be taken when using decision-support systems which have been developed in other centers. Neural networks offer a method to evaluate differences between databases. With the use of larger neighborhoods, the effects of the differences on the accuracy of the classification can be partly diminished.