NEURAL NETWORK BASED AUTOMATIC DIAGNOSIS OF CHILDREN WITH BRAIN DYSFUNCTION-Reference-Cited by-同舟云学术

NEURAL NETWORK BASED AUTOMATIC DIAGNOSIS OF CHILDREN WITH BRAIN DYSFUNCTION

Published:2001-08 Issue:04 Volume:11 Page:361-369
ISSN:0129-0657
Container-title:International Journal of Neural Systems
language:en
Short-container-title:Int. J. Neur. Syst.

Author:

CHO SUNGZOON¹,SHIN MIN SUP²

Affiliation:

1. Department of Industrial Engineering, Seoul National University, San 56-1 Shinrimdong, Seoul, 151-744, Korea

2. Department of Child-Adolescent Psychiatry, Seoul National University, 28 Yongondong, Seoul, 110-744, Korea

Abstract

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.

Publisher

World Scientific Pub Co Pte Lt

Subject

Computer Networks and Communications,General Medicine

Link

https://www.worldscientific.com/doi/pdf/10.1142/S0129065701000825

Reference10 articles.

1. A neural network approach to the classification of autism

2. Multiple disorder diagnosis with adaptive competitive neural networks

3. Artificial Neural Network to Assist Psychiatric Diagnosis

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