Neural network based automatic diagnosis of children with brain dysfunction.
This paper proposes the use of multilayer perceptron for brain dysfunction diagnosis. The performance of MLP was better than that of Discriminant Analysis and Decision Tree classifiers, with an 85% accuracy rate in an experimental test involving 332 subjects. In addition, the neural network employing Bayesian learning was able to identify the most important input variable. These two results demonstrate that the neural network can be effectively used in the diagnosis of children with brain dysfunction.
Cho
S
S
Department of Industrial Engineering, Seoul National University, San 56-1 Shinrimdong, Seoul, 151-744, Korea. zoon@snu.ac.kr
Shin
M S
MS
D001289:Attention Deficit Disorder with Hyperactivity / Q000175:diagnosis* / Q000523:psychology; D001499:Bayes Theorem; D002648:Child; D003663:Decision Trees; D006801:Humans; D016571:Neural Networks, Computer