Finding the Forest in the Trees: Using Machine Learning and Online Cognitive and Perceptual Measures to Predict Adult Autism Diagnosis

Traditional subjective measures are limited in the insight they provide into underlying behavioral differences associated with autism and, accordingly, their ability to predict diagnosis. Performance-based measures offer an attractive alternative, as they are designed to capture related neuropsychological constructs more directly and objectively. We used machine learning to classify autistic/non-autistic adults using data from online tasks measuring multisensory perception, emotion recognition, and executive function. Not only were these measures able to predict autism in a late-diagnosed population known to be particularly difficult to identify, their combination with the most popular screening questionnaire enhanced its predictive accuracy (reaching 92% together). Many variables in which significant group differences were not detected had predictive value in combination, suggesting complex latent relationships. Machine learning’s ability to harness these connections and pinpoint the most crucial features for prediction could allow optimization of a screening tool that offers a unique marriage of predictive accuracy and accessibility.