Is neural network better than statistical methods in diagnosis of acute appendicitis?
Three statistical classification methods: discriminant analysis, logistic regression analysis and cluster analysis were compared with the back-propagation neural network algorithm in the diagnosis of acute appendicitis. The differences in the classification accuracy, which were evaluated with the receiver operating characteristic (ROC) curve were small, though discriminant analysis and back-propagation showed slightly better results than the other methods. The agreement of the methods on the diagnosis increased the accuracy of the classification, so that the number of misclassified cases reduced. The back-propagation neural network offers a good choice for statistical classification methods, but it was not found to be better than them. The use of several methods and their agreement as the basis of the diagnosis seems to give the best results for this diagnostic classification problem.
Pesonen
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Department of Computer Science and Applied Mathematics, University of Kuopio, Finland.
D001064:Appendicitis / Q000175:diagnosis*; D016000:Cluster Analysis; D003936:Diagnosis, Computer-Assisted; D016002:Discriminant Analysis; D005387:Finland; D006801:Humans; D016015:Logistic Models; D016571:Neural Networks, Computer; D012372:ROC Curve; D013223:Statistics as Topic