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Intellectual disability (ID) is a neurodevelopmental condition characterized by deficits in intellectual and adaptive functioning that manifest during the developmental period. This condition encompasses challenges in reasoning, memory, problem-solving, and learning, accompanied by impairments in everyday life skills, such as communication, self-care, and social interactions. Intellectual disability affects approximately 1% of the population in the United States, impacting an estimated 5...
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Explainability Enhanced Machine Learning Model for Classifying Intellectual Disability and

Tong Min Kim1, Young-Hoon Kim2, Sung-Hee Song3

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Summary

Explainable AI models accurately classify intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD) from psychological reports, improving diagnostic insights for physicians.

Keywords:
Attention-Deficit Hyperactivity Disorder Psychological Test ReportsExplainable ModelIntellectual DisabilityMachine LearningNatural Language ProcessingNeurodevelopmental Disorder

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Area of Science:

  • Artificial Intelligence in Medicine
  • Natural Language Processing (NLP)

Background:

  • Psychological test reports are crucial for diagnosing intellectual disability (ID) and attention-deficit/hyperactivity disorder (ADHD).
  • Current reports are often unstructured, subjective, and prone to human error, leading to diagnostic challenges.
  • Physicians may not review full reports, and report volume often trails diagnostic needs.

Purpose of the Study:

  • To develop explainable predictive models for classifying ID and ADHD from patient reports.
  • To address limitations of traditional report analysis using NLP and AI.

Main Methods:

  • Utilized NLP to analyze 1,475 patient reports, incorporating physician diagnoses.
  • Employed SHAP and permutation importance for feature selection, ensuring model explainability.
  • Developed an n-gram feature-based search system to reconstruct human-readable text from model outputs.

Main Results:

  • Achieved a maximum model accuracy of 0.92 in classifying ID and ADHD.
  • Successfully restored 80 human-readable texts from four distinct models.
  • Demonstrated accurate classification even with limited report data.

Conclusions:

  • The developed models accurately classify ID and ADHD, offering explainable predictions.
  • Enhanced model explainability aids physician understanding and supports evidence-based decision-making.
  • This approach offers a scalable solution for improving diagnostic efficiency and accuracy.