Feature reduction using SVM-RFE technique to detect autism spectrum disorder
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Feature reduction using SVM‑RFE technique to detect autism spectrum disorder Priya Mohan1 · Ilango Paramasivam2 Received: 22 May 2020 / Revised: 10 September 2020 / Accepted: 24 September 2020 © Springer-Verlag GmbH Germany, part of Springer Nature 2020
Abstract 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. Keywords Autism spectrum disorder (ASD) · IBk (K-nearest neighbor) · Naïve Bayes · Recursive feature elimination (RFE) · LibSVM · SVMAttributeEval
1 Introduction Autism or Autism Spectrum Disorder (ASD) is a developmental disorder of the brain that consists of a range of conditions like challenges in exhibiting social skills, repetitive behaviors, lack of speech and nonverbal communication along with notable strengths and differences. According to the data released by the Centers for Disease Control (CDC) on the prevalence of autism, the survey study has identified 1 in 59 children as having autism spectrum disorder (ASD) as on April 26th 2018 [1]. This implies that early diagnosis of ASD can lead to better outcomes by enabling the families with ASD to avail early intervention services between 18 * Priya Mohan [email protected] 1
Department of Computer Science, Bharathiar University, Coimbatore 641046, India
Department of Computer Science and Engineering, PSG Institute of Technology and Applied Research, Coimbatore 641062, India
2
and 24 months of age for the affected autistic individual. Several screening instruments have been developed to gather quick information about a child’s social and communicative development viz., Checklist for Autism in Toddlers (CHAT), the Modified Checklist for Autism in Tod
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