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The potential of evaluating shape drawing using machine learning for predicting high autistic traits.

Yoshimasa Ohmoto1, Kazunori Terada2, Hitomi Shimizu3

  • 1Faculty of Informatics, Department of Behavior Informatics, Shizuoka University, Shizuoka, Japan.

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Summary

Machine learning accurately predicts high autistic traits by analyzing shape drawing. This method shows promise for early autism spectrum condition screening in children.

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Area of Science:

  • Neuroscience
  • Developmental Psychology
  • Computer Science

Background:

  • Children with high autistic traits often struggle with drawing, impacting social adaptability.
  • Machine learning offers objective evaluation of drawing processes and products.
  • This study explores machine learning's potential in predicting autistic traits from drawing data.

Purpose of the Study:

  • To assess the efficacy of machine learning in evaluating shape drawing for predicting high autistic traits.
  • To develop a predictive model for identifying individuals with high autistic traits based on drawing performance.

Main Methods:

  • Seventy-three boys and sixty-three girls (aged ~5) drew shapes (triangle, square, sun).
  • A support vector machine (SVM) algorithm with a linear kernel was used for classification.
  • Fine motor data (pen tablet) and eye movements (webcam) were recorded and analyzed.

Main Results:

  • SVM models achieved >85% accuracy, sensitivity, and specificity in classifying high autistic traits.
  • Specificity reached 100% for all models, and 100% for the inverted equilateral triangle model.

Conclusions:

  • Shape drawing analysis using machine learning shows significant potential for predicting high autistic traits.
  • Drawing skills may serve as a viable screening tool for autism spectrum conditions.
  • Further research with diverse shapes is recommended to validate these findings.