Population-based detection of children ASD/ADHD comorbidity from atypical sensory processing

Abstract

AbstractComorbidity between neurodevelopmental disorders is common, especially between autism spectrum disorder (ASD) and attention deficit/hyperactivity disorder (ADHD). This study aimed to detect overlapped sensory processing alterations in a sample of children and adolescents diagnosed with both ASD and ADHD. A collection of 42 standard and 8 proposed machine learning classifiers, 22 feature selection methods and 19 unbalanced classification strategies were applied on the 6 standard question groups of the Sensory Profile-2 questionnaire. The relatively low performance achieved by state-of-the-art classifiers led us to propose the feature population sum classifier, a probabilistic method based on class and feature value populations, designed for datasets where features are discrete numeric answers to questions in a questionnaire. The proposed method achieves the best kappa and accuracy, 60% and 82.5%, respectively, reaching 68% and 86.5% combined with backward sequential feature selection, with false positive and negative rates below 15%. Since the SP2 questionnaire can be filled by parents for children from three years, our prediction can alert the clinicians with an early diagnosis in order to apply early interventions.