Abstract:
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.
Keywords
Database - Acute Appendicitis - Diagnosis - Learning Vector Quantization - Neural
Network - Neighborhood
