  Autism Spectrum Disorder (ASD) is a severe neuropsychiatric disorder that
affects intellectual development, social behavior, and facial features, and the
number of cases is still significantly increasing. Due to the variety of
symptoms ASD displays, the diagnosis process remains challenging, with numerous
misdiagnoses as well as lengthy and expensive diagnoses. Fortunately, if ASD is
diagnosed and treated early, then the patient will have a much higher chance of
developing normally. For an ASD diagnosis, machine learning algorithms can
analyze both social behavior and facial features accurately and efficiently,
providing an ASD diagnosis in a drastically shorter amount of time than through
current clinical diagnosis processes. Therefore, we propose to develop a hybrid
architecture fully utilizing both social behavior and facial feature data to
improve the accuracy of diagnosing ASD. We first developed a Linear Support
Vector Machine for the social behavior based module, which analyzes Autism
Diagnostic Observation Schedule (ADOS) social behavior data. For the facial
feature based module, a DenseNet model was utilized to analyze facial feature
image data. Finally, we implemented our hybrid model by incorporating different
features of the Support Vector Machine and the DenseNet into one model. Our
results show that the highest accuracy of 87% for ASD diagnosis has been
achieved by our proposed hybrid model. The pros and cons of each module will be
discussed in this paper.
