Abstract
A nonlinear pattern recognition system, neural network technology, was explored for its utility in assisting in the classification of autism. It was compared with a more traditional approach, simultaneous and stepwise linear discriminant analyses, in terms of the ability of each methodology to both classify and predict persons as having autism or mental retardation based on information obtained from a new structured parent interview: the Autistic Behavior Interview. The neural network methodology was superior to discriminant function analysis both in its ability to classify groups (92 vs. 85%) and to generalize to new cases that were not part of the training sample (92 vs. 82%). Interrater and test-retest reliabilities and measures of internal consistency were satisfactory for most of the subscales in the Autistic Behavior Interview. The implications of neural network technology for diagnosis, in general, and for understanding of possible core deficits in autism are discussed.
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The authors are gratefully indebted to Dov. J. Shazeer, Group Leader, Image Recognition Systems,Charles Stark Draper Laboratory, Cambridge, Massachusetts, for initially suggesting the use of neural networks for problems in diagnosis and for his considerate guidance in helping the first author to understand the complexity of this methodology. In addition, we are also indebted to the staff at NeuralWare, Inc., Pittsburgh, Pennsylvania, in particular, Bob Everly, for his kind advice, patience, and assistance to the first author and to Allan Reiss, and Enid G. Wolf-Schein, for their comments on the Autistic Behavior Interview. This work was supported by funds from the New York State Office of Mental Retardation and Developmental Disabilities.
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Cohen, I.L., Sudhalter, V., Landon-Jimenez, D. et al. A neural network approach to the classification of autism. J Autism Dev Disord 23, 443–466 (1993). https://doi.org/10.1007/BF01046050
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DOI: https://doi.org/10.1007/BF01046050