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

Comorbidity 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.

keywords: Machine Learning, Classification, ASD, ADHD, Sensory profile, Feature Selection