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This is a Quantitative Study study.

October 27, 2025ChildrenOpen Access

Machine Learning for ADHD Diagnosis: Feature Selection from Parent Reports, Self-Reports and Neuropsychological Measures

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Machine learning reveals key predictors for ADHD diagnosis in children, suggesting improved clinical tools may enhance diagnosis accuracy.

High classification accuracy was achieved in ADHD diagnosis using machine learning techniques.
Key predictors include features from parent reports and measures of executive dysfunction.
Models were validated through nested cross-validation in a sample of 255 Taiwanese children.
Findings suggest integrating various reports can improve ADHD diagnostic accuracy in practice.

Dai et al. (2025) studied this question.

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Machine learning reveals key predictors for ADHD diagnosis in children, suggesting improved clinical tools may enhance diagnosis accuracy.