Machine learning-based method for evaluating and predicting ASD

Abstract

The present invention discloses a machine learning-based method for evaluating and predicting ASD, comprising the following steps: S1. data collection: using an eye tracker to separately collect eye movement data that an eye ball scans a human face when persons participating in a test watch a human face image, wherein the persons participating in the test comprise individuals who have ASD and normal individuals; S2. feature extraction: dividing the human face image into different areas according to collected eye movement coordinate data, extracting a feature from the original data collected by the eye tracker, and making a mark; S3. classifier training: training a classifier by using a marked feature, to obtain a classifier model for predicting ASD; and S4. prediction: testing on a test subject by using the classifier model for predicting ASD acquired in step S3, to evaluate and predict severity of autism of the test subject. The present invention may be considered as a supplementary method of ASD evaluation, so that ASD evaluation and prediction in an early stage is more accurate and convenient.

Description

technical field [0001] The present invention relates to the field of image processing, and more specifically, relates to a method for evaluating and predicting autism based on eye movement patterns based on machine learning. It is a method based on machine learning technology, which is based on the saccade pattern of the subject when observing a face picture. To evaluate methods for predicting autism spectrum disorder. Background technique [0002] Social attention to autism spectrum disorder (autismspectrumdisorder, ASD) has risen sharply in the past few years. In the United States, 1 in 68 people will suffer from autism. Although the existing ASD assessment methods are very effective, they are time-consuming and labor-intensive; and most of the diagnostic methods mainly evaluate the three aspects of language communication impairment, social communication impairment, and repetitive stereotyped behavior. Now the most widely used measurement methods include the Autism Diagno...

AI Technical Summary

Problems solved by technology

The disadvantage of the AOI method is that the areas it divides are relatively large, and these areas are not further distinguished

And only in the statistical sense to distinguish whether it is statistically significant, no further predictions are made

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