Autism Spectrum Disorder Prediction by an Explainable Deep Learning Approach

Autism Spectrum Disorder (ASD) is a developmental disorder whose symptoms become noticeable in early years of the age though it can be present in any age group. ASD is a mental disorder which affects the communicational, social and non-verbal behaviors. It cannot be cured completely but can be reduced if detected early. An early diagnosis is hampered by the variation and severity of ASD symptoms as well as having symptoms commonly seen in other mental disorders as well. Nowadays, with the emergence of deep learning approaches in various fields, medical experts can be assisted in early diagnosis of ASD. It is very difficult for a practitioner to identify and concentrate on the major feature's leading to the accurate prediction of the ASD and this arises the need for having an automated approach. Also, presence of different symptoms of ASD traits amongst toddlers directs to the creation of a large feature dataset. In this study, we propose a hybrid approach comprising of both, deep learning and Explainable Artificial Intelligence (XAI) to find the most contributing features for the early and precise prediction of ASD. The proposed framework gives more accurate prediction along with the recommendations of predicted results which will be a vital aid clinically for better and early prediction of ASD traits amongst toddlers.