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An explainable machine learning-based approach to predicting treatment response for neurofeedback in ADHD.

Reza Hoseini1, Ahmad Shalbaf2

  • 1Department of Biomedical Engineering and Medical Physics, School of Medicine, Shahid Beheshti University of Medical Sciences , Tehran, Iran.

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|December 4, 2025
PubMed
Summary

This study developed an explainable AI framework to predict attention-deficit hyperactivity disorder (ADHD) treatment response. The model achieved 88.3% accuracy using only seven key features, enabling personalized ADHD interventions.

Keywords:
Attention-deficit hyperactivity disorderExplainableMachine learningNeurofeedback

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Area of Science:

  • Neuroscience
  • Artificial Intelligence
  • Medical Informatics

Background:

  • Attention-deficit hyperactivity disorder (ADHD) is a complex neurodevelopmental disorder requiring personalized treatment due to its heterogeneity.
  • Existing predictive models for ADHD treatment response often lack transparency, hindering clinical trust and adoption.
  • Early and effective intervention is crucial for mitigating the long-term effects of untreated ADHD.

Purpose of the Study:

  • To introduce a novel, explainable machine learning framework for predicting neurofeedback treatment response in individuals with ADHD.
  • To enhance personalized ADHD intervention by providing transparent and clinically actionable insights into treatment prediction.
  • To identify key predictive features for neurofeedback treatment response using advanced feature selection and explainability techniques.

Main Methods:

  • Utilized a dataset of 72 ADHD patients from the TDBRAIN database, including 78 features (demographic, behavioral, NEO-FFI).
  • Employed a hierarchical feature selection approach: initial statistical filtering followed by Sequential Forward Selection (SFS) with four reduction methods.
  • Developed and evaluated predictive models using five classifiers (Random Forest, SVM, Logistic Regression, ANN, Adaptive Boosting), with SHAP values for interpretability.

Main Results:

  • A hierarchical feature selection approach, culminating in SFS, identified seven optimal features that significantly improved the Random Forest model's accuracy to 88.3% ± 6.8%.
  • Key predictive features included specific NEO-FFI questions and education level, with five of the seven SFS features identified as highly significant by SHAP values.
  • The explainable AI framework provided both global feature importance and local explanations, demonstrating model consistency and highlighting critical predictive factors.

Conclusions:

  • The developed transparent machine learning framework achieves high prediction performance for ADHD neurofeedback treatment response, surpassing previous studies.
  • The explainable nature of the model fosters trust and supports personalized, data-driven medical decisions for ADHD intervention.
  • This approach moves beyond 'black-box' predictions, offering clinically actionable insights for tailoring neurofeedback therapies to individual ADHD patients.