  Autism spectrum disorder (ASD) can be defined as a neurodevelopmental
disorder that affects how children interact, communicate and socialize with
others. This disorder can occur in a broad spectrum of symptoms, with varying
effects and severity. While there is no permanent cure for ASD, early detection
and proactive treatment can substantially improve the lives of many children.
Current methods to accurately diagnose ASD are invasive, time-consuming, and
tedious. They can also be subjective perspectives of a number of clinicians
involved, including pediatricians, speech pathologists, psychologists, and
psychiatrists. New technologies are rapidly emerging that include machine
learning models using speech, computer vision from facial, retinal, and brain
MRI images of patients to accurately and timely detect this disorder. Our
research focuses on computational linguistics and machine learning using speech
data from TalkBank, the world's largest spoken language database. We used data
of both ASD and Typical Development (TD) in children from TalkBank to develop
machine learning models to accurately predict ASD. More than 50 features were
used from specifically two datasets in TalkBank to run our experiments using
five different classifiers. Logistic Regression and Random Forest models were
found to be the most effective for each of these two main datasets, with an
accuracy of 0.75. These experiments confirm that while significant
opportunities exist for improving the accuracy, machine learning models can
reliably predict ASD status in children for effective diagnosis.
