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Autism Spectrum Disorder (ASD) is a severe neuropsychiatric disorder thataffects intellectual development, social behavior, and facial features, and thenumber of cases is still significantly increasing. Due to the variety ofsymptoms ASD displays, the diagnosis process remains challenging, with numerousmisdiagnoses as well as lengthy and expensive diagnoses. Fortunately, if ASD isdiagnosed and treated early, then the patient will have a much higher chance ofdeveloping normally. For an ASD diagnosis, machine learning algorithms cananalyze both social behavior and facial features accurately and efficiently,providing an ASD diagnosis in a drastically shorter amount of time than throughcurrent clinical diagnosis processes. Therefore, we propose to develop a hybridarchitecture fully utilizing both social behavior and facial feature data toimprove the accuracy of diagnosing ASD. We first developed a Linear SupportVector Machine for the social behavior based module, which analyzes AutismDiagnostic Observation Schedule (ADOS) social behavior data. For the facialfeature based module, a DenseNet model was utilized to analyze facial featureimage data. Finally, we implemented our hybrid model by incorporating differentfeatures of the Support Vector Machine and the DenseNet into one model. Ourresults show that the highest accuracy of 87% for ASD diagnosis has beenachieved by our proposed hybrid model. The pros and cons of each module will bediscussed in this paper.

Proposing a System Level Machine Learning Hybrid Architecture and Approach for a Comprehensive Autism Spectrum Disorder Diagnosis