Date of Award


Document Type


Degree Name

Master of Science in Computer Science


School of Computer Science and Engineering

First Reader/Committee Chair

Voigt, Kerstin


This thesis uses autoencoders to explore the possibility of reducing the length of the Autism Diagnostic Observation Schedule (ADOS), which is a series of tests and observations used to diagnose autism spectrum disorders in children, adolescents, and adults of different developmental levels. The length of the ADOS, directly and indirectly, causes barriers to its access for many individuals, which means that individuals who need testing are unable to get it. Reducing the length of the ADOS without significantly sacrificing its accuracy would increase its accessibility. The autoencoders used in this thesis have specific connections between layers that mimic the sectional structure of the original ADOS. Autoencoders reduce the length of the ADOS by conducting its dimensionality through combining original variables into new variables. By examining the weights of variables entering the reduced diagnostic, this thesis explores which variables are prioritized and deprioritized by the autoencoder. These information yields insights as to which variables, and underlying concepts, should prioritize in a shorter ADOS. After training, all autoencoders used were able to reduce dimensionality with minimal accuracy losses. Examination of weights yielded many keen insights as to which ADOS variables are the least important to their modules and can thus be eliminated or deprioritized in a reduced diagnostic. In particular, the observation of self-injurious behavior was declared entirely unnecessary in the first three modules of the ADOS, a finding that corroborates other recent experimental results in the domain. This observation suggests that the solutions converged upon by the model have real-world significance.