fNIRS Classification of Adults With ADHD Enhanced by Feature Selection

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.