Mobile detection of autism through machine learning on home video: A development and prospective validation study
The standard approaches to diagnosing autism spectrum disorder (ASD) evaluate between 20 and 100 behaviors and take several hours to complete. This has in part contributed to long wait times for a diagnosis and subsequent delays in access to therapy.
We hypothesize that the use of machine learning analysis on home video can speed the diagnosis without compromising accuracy.
We have analyzed item-level records from 2 standard diagnostic instruments to construct machine learning classifiers optimized for sparsity, interpretability, and accuracy.
In the present study, we prospectively test whether the features from these optimized models can be extracted by blinded nonexpert raters from 3-minute home videos of children with and without ASD to arrive at a rapid and accurate machine learning autism classification...
Read the entire journal article here.
Using the data collected in the study above, our assignment was to create our own machine learning models from scratch in order to classify patients as having ASD or not. The two models that we were asked to implement were a decision tree classifier, as well as a random forest classifier. Read the full report of the methods and implementation details below.
Technical Report
