COMPARATIVE ANALYSIS OF BP-NN, SVM, LVQ AND RBF FOR DIAGNOSIS OF AUTISM SPECTRUM DISORDER(ASD) BASED ON DSM-IV CRITERIA

Abstract

Training artificial networks is a complex task of especially importance in the supervised learning field of research. Autism spectrum disorder is neurodevelopment disorder having qualitative impairments in social interaction, communication and repetitive /restrictive patterns of behavior. DSM-IV criteria is used for diagnosing autism spectrum disorder. DSM-IV (Diagnostic and Statistical Manual of Mental Disorders, 4th Edition) describes all mental health disorders for both children and adults which has been published by American Psychiatric Association .We intend to show the superiority (accuracy and error rate) of the Back-propagation Neural Network over other more ―standard algorithms in neural network training. In this work we tackle the problem with four algorithms, Back-propagation Neural Network (BPNN), LVQ (learning vector quantization), SVM (support vector machine) and RBF(radial basis Function) those are implemented using MATLAB tool and try to over a set of results that could hopefully faster future comparisons by using a standard dataset of autistic children and further presents the results. Our conclusions clearly establish the advantages of the BPNN algorithm over the other algorithms in the diagnosing the autistic children.