fNIRS Classification of Adults with ADHD Enhanced by Feature Selection DOI Creative Commons
Min Hong,

Suh-Yeon Dong,

Roger S. McIntyre

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2024, Номер 33, С. 220 - 231

Опубликована: Дек. 24, 2024

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 functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques differentiate between healthy controls (N=75) ADHD individuals (N=120). Efficient feature selection high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose hybrid method that combines wrapper-based embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed facilitated streamlined hyperparameter tuning data, thereby reducing the number of features while enhancing HbO from combined frontal temporal regions were key, models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy MCC (78.36%), GDR (88.45%). outcomes this highlight promising potential combining ML as diagnostic tools clinical settings, offering pathway reduce manual intervention.

Язык: Английский

Explainable Multi-Layer Dynamic Ensemble Framework Optimized for Depression Detection and Severity Assessment DOI Creative Commons

Dillan Imans,

Tamer Abuhmed, Meshal Alharbi

и другие.

Diagnostics, Год журнала: 2024, Номер 14(21), С. 2385 - 2385

Опубликована: Окт. 25, 2024

Depression is a pervasive mental health condition, particularly affecting older adults, where early detection and intervention are essential to mitigate its impact. This study presents an explainable multi-layer dynamic ensemble framework designed detect depression assess severity, aiming improve diagnostic precision provide insights into contributing factors.

Язык: Английский

Процитировано

2
Locality-constrained robust discriminant non-negative matrix factorization for depression detection: An fNIRS study DOI
Yushan Wu, Jitao Zhong, Lu Zhang

и другие.

Neurocomputing, Год журнала: 2024, Номер unknown, С. 128887 - 128887

Опубликована: Ноя. 1, 2024

Язык: Английский

Процитировано

0
fNIRS Classification of Adults with ADHD Enhanced by Feature Selection DOI Creative Commons
Min Hong,

Suh-Yeon Dong,

Roger S. McIntyre

и другие.

IEEE Transactions on Neural Systems and Rehabilitation Engineering, Год журнала: 2024, Номер 33, С. 220 - 231

Опубликована: Дек. 24, 2024

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 functional near-infrared spectroscopy (fNIRS) during verbal fluency tasks in conjunction with machine learning (ML) techniques differentiate between healthy controls (N=75) ADHD individuals (N=120). Efficient feature selection high-dimensional fNIRS datasets is crucial for improving accuracy. To address this, we propose hybrid method that combines wrapper-based embedded approach, termed Bayesian-Tuned Ridge RFECV (BTR-RFECV). The proposed facilitated streamlined hyperparameter tuning data, thereby reducing the number of features while enhancing HbO from combined frontal temporal regions were key, models achieving precision (89.89%), recall (89.74%), F-1 score (89.66%), accuracy MCC (78.36%), GDR (88.45%). outcomes this highlight promising potential combining ML as diagnostic tools clinical settings, offering pathway reduce manual intervention.

Язык: Английский

Процитировано

0