  Currently, every 1 in 54 children have been diagnosed with Autism Spectrum
Disorder (ASD), which is 178% higher than it was in 2000. An early diagnosis
and treatment can significantly increase the chances of going off the spectrum
and making a full recovery. With a multitude of physical and behavioral tests
for neurological and communication skills, diagnosing ASD is very complex,
subjective, time-consuming, and expensive. We hypothesize that the use of
machine learning analysis on facial features and social behavior can speed up
the diagnosis of ASD without compromising real-world performance. We propose to
develop a hybrid architecture using both categorical data and image data to
automate traditional ASD pre-screening, which makes diagnosis a quicker and
easier process. We created and tested a Logistic Regression model and a Linear
Support Vector Machine for Module 1, which classifies ADOS categorical data. A
Convolutional Neural Network and a DenseNet network are used for module 2,
which classifies video data. Finally, we combined the best performing models, a
Linear SVM and DenseNet, using three data averaging strategies. We used a
standard average, weighted based on number of training data, and weighted based
on the number of ASD patients in the training data to average the results,
thereby increasing accuracy in clinical applications. The results we obtained
support our hypothesis. Our novel architecture is able to effectively automate
ASD pre-screening with a maximum weighted accuracy of 84%.
