Using Diagnostic Information to Develop a Machine Learning Application for the Effective Screening of Autism Spectrum Disorders

Abstract

A 2-Class Support Vector Machine (SVM) classification model was developed by means of machine learning techniques and text analysis of Autism Spectrum Disorders (ASD) diagnostic reports. The ability of the 2-Class SVM application to screen for ASD is compared with other screening instruments: Gillian Autism Rating Scale - Second Edition [25], Social Communication Questionnaire [51] and Social Responsiveness Scale [11]. It was also cross-validated and refined based on a sample (n = 221). The classification performance of the SVM application was relatively better compared to the other instruments (accuracy = 83.7 %, precision = 98.8 %, sensitivity = 83.3 %, specificity = 88.9 %). A 1-Class SVM..