Comparison of Random Forest, XGBoost, and RBF SVM for ADHD Detection
DOI:
https://doi.org/10.17979/ja-cea.2026.47.13843Keywords:
ADHD, EEG, Machine Learning, Random Forest, SVM-RBF, XGBoost, Automated Classification, Computer-Aided Diagnosis, Computational Neuroscience, EEG Biomarkers, Early DetectionAbstract
The diagnosis of Attention-Deficit/Hyperactivity Disorder (ADHD) relies primarily on subjective assessments, such as clinical interviews and behavioral questionnaires, which may lead to diagnostic bias and inconsistencies. In this context, electroencephalographic (EEG) signals have emerged as a promising tool due to the alterations observed in their spectral patterns and functional dynamics in individuals with ADHD. This study explores the application of machine learning techniques for ADHD detection based on EEG signals. Specifically, three classification models were implemented and compared: Random Forest, Support Vector Machines with a Radial Basis Function kernel (SVM-RBF), and XGBoost. Their performance was evaluated using accuracy, precision, and sensitivity metrics to assess their discriminative capability. The results demonstrate the potential of artificial intelligence as a support tool for traditional clinical diagnosis, although they remain insufficient for fully autonomous clinical applications.
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Copyright (c) 2026 Adrián Jiménez-García, Raquel García-Ausín, Jesús Enrique Sierra García, Bruno Baruque-Zanón
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