Machine learning approach for early detection of autism by combining questionnaire and home video screening

TL;DR

This paper presents a machine learning-based autism screening tool that combines questionnaire data and home video analysis, offering a low-cost, efficient, and accurate alternative to traditional methods.

Contribution

The study introduces a novel integrated screening approach using ML on combined questionnaire and video data, improving accuracy over existing tools.

Findings

Significant accuracy improvement over standard screening tools.

Effective handling of sparse and imbalanced data.

Inclusion of inconclusive results enhances overall screening performance.

Abstract

Existing screening tools for early detection of autism are expensive, cumbersome, time-intensive, and sometimes fall short in predictive value. In this work, we apply Machine Learning (ML) to gold standard clinical data obtained across thousands of children at risk for autism spectrum disorders to create a low-cost, quick, and easy to apply autism screening tool that performs as well or better than most widely used standardized instruments. This new tool combines two screening methods into a single assessment, one based on short, structured parent-report questionnaires and the other on tagging key behaviors from short, semi-structured home videos of children. To overcome the scarcity, sparsity, and imbalance of training data, we apply creative feature selection, feature engineering, and novel feature encoding techniques. We allow for inconclusive determination where appropriate in order to boost screening accuracy when conclusive. We demonstrate a significant accuracy improvement over standard screening tools in a clinical study sample of 162 children.