Comparison of different neural network algorithms in the diagnosis of acute appendicitis.
Four different neural network algorithms, binary adaptive resonance theory (ART1), self-organizing map, learning vector quantization and back-propagation, were compared in the diagnosis of acute appendicitis with different parameter groups. The results show that supervised learning algorithms learning vector quantization and back-propagation were better than unsupervised algorithms in this medical decision making problem. The best results were obtained with the learning vector quantization. The self-organizing map algorithm showed good specificity, but this was in conjunction with lower sensitivity. The best parameter group was found to be the clinical signs. It seems beneficial to design a decision support system which uses these methods in the decision making process.
Pesonen
E
E
University of Kuopio, Department of Computer Science and Applied Mathematics, Finland. Erkki.Pesonen@uku.fi
Eskelinen
M
M
Juhola
M
M
D000293:Adolescent; D000328:Adult; D000368:Aged; D000465:Algorithms; D001064:Appendicitis / Q000175:diagnosis*; D003936:Diagnosis, Computer-Assisted; D005260:Female; D006801:Humans; D008297:Male; D008875:Middle Aged; D016571:Neural Networks, Computer; D012680:Sensitivity and Specificity