fNIRS Classification of Adults With ADHD Enhanced by Feature Selection
Bibliographic record
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.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".