1AbstractCurrently Autism Spectrum Disorder (ASD) is diagnosed via the combination of multiple medical tools and screening tests that require extensive amounts of time and money. Autism diagnosis can be formulated as a typical machine learning classification problem between ASD patients and a control group consisting of neurotypical individuals. In order for this to yield accurate results, large datasets with different modalities are required. However, the unavailability of such robust datasets stands as a threat to this automated diagnosis. To resolve this, we propose a method of Autism Classification using Visual and Behavioral Data. The proposed technique relates datasets of two modalities (visual and behavioral) collected from similar participants by generating common attributes among the records and distributing these records into sub classes. Then records within these subclasses are combined to form an integrated dataset. Finally, decision level fusion is performed on the multimodal data. The main contribution of our work can be outlined as follows: an accuracy of 97.57% in autism classification has been obtained from the integrated data, which is higher than detection from only visual data, we have shown that combining data within sub classes based on common attributes is more accurate than combining them arbitrarily, and finally, we have introduced a novel, integrated multimodal dataset in the ASD domain.