Feature reduction using SVM-RFE technique to detect autism spectrum disorder

Autism Spectrum Disorder (ASD) is a developmental disorder characterized by difficulties in social interaction, communication, and restricted or repetitive patterns of thought and behaviour. Diagnosing ASD is important since it is a life long condition and early diagnosis of ASD has a great deal of importance in terms of controlling the disease. This research work focuses on the analysis of the features that are vital in diagnosing the symptoms of ASD in an individual and to help in the early identification of ASD. The autism dataset for this research work is taken from the UCI repository. The proposed method, SVMAttributeEval, assigns feature weight to the features and the features are ranked based on their importance. The recursive Feature Elimination method is applied and the performance of the classification algorithms LibSVM, IBk, and Naïve Bayes for the reduced feature subsets selected by the wrapper method is measured. The empirical results show an improvement in the accuracy of the classifiers on the removal of the least significant features with feature reduction of 60% achieved against the original feature set. The performance of the classification algorithms has significantly improved for the reduced feature subset of ASD. The LibSVM classification algorithm achieves 93.26% accuracy, IBk (92.3%), and Naïve Bayes (91.34%) for the selected feature subset as compared to the values achieved for the whole feature set.