1. fNIRS Classification of Adults With ADHD Enhanced by Feature Selection
- Author
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Minyeong Hong, Suh-Yeon Dong, Roger S. McIntyre, Soon-Kiat Chiang, and Roger Ho
- Subjects
Verbal fluency task ,functional near- infrared spectroscopy ,attention-deficit/hyperactivity disorder ,machine learning ,feature selection ,Medical technology ,R855-855.5 ,Therapeutics. Pharmacology ,RM1-950 - Abstract
Adult attention deficit hyperactivity disorder (ADHD), a prevalent psychiatric disorder, significantly impacts social, academic, and occupational functioning. However, it has been relatively less prioritized compared to childhood ADHD. This study employed a functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques to differentiate between healthy controls (N =75) and ADHD individuals (N =120). Efficient feature selection in high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose a hybrid feature selection method that combines a wrapper-based and embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed method facilitated streamlined feature selection and hyperparameter tuning in high-dimensional data, thereby reducing the number of features while enhancing accuracy. HbO features from the combined frontal and temporal regions were key, with the models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy (89.74%), MCC (78.36%), and GDR (88.45%). The outcomes of this study highlight the promising potential of combining fNIRS with ML as diagnostic tools in clinical settings, offering a pathway to significantly reduce manual intervention.
- Published
- 2025
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