Functional Near-Infrared Spectroscopy Combined with Machine Learning for Exploratory Classification of Autism Spectrum Disorder in Young Children.

Early identification of autism spectrum disorder (ASD) may facilitate timely intervention, while diagnosis remains clinically based. This exploratory study evaluated fNIRS-derived features using multiple machine-learning algorithms to compare observed classification performance between young children aged 2-6 years with ASD and typically developing (TD) children. β-activation values from eight channels were used as features to differentiate the ASD and TD groups. Two paradigm-specific analytic datasets were analyzed. Paradigm 1 comprised three tasks and included 60 participants (29 children with ASD and 31 TD children), with 24 β features per participant. Paradigm 2 comprised two tasks and included 58 participants (28 children with ASD and 30 TD children), with 16 β features per participant. Leave-one-out cross-validation was used to partition the data, and normalization and principal component analysis were applied for feature extraction. Seven classifiers-K-nearest neighbors (KNN), logistic regression (LR), support vector machine (SVM), decision tree (DT), random forest (RF), gradient boosting decision trees (GBDT), and XGBoost-were evaluated using accuracy, specificity, sensitivity, and area under the curve (AUC), and permutation testing was used to assess whether the observed classification performance exceeded that expected by chance. In Paradigm 1, KNN yielded the highest observed accuracy (0.567), whereas DT yielded the highest observed AUC (0.582). Paradigm 2 yielded numerically higher observed performance for several metrics, but no classifier significantly exceeded chance. These findings demonstrate the procedural feasibility of acquiring and analyzing task-based fNIRS data; however, independent diagnostic value beyond age and sex was not established and requires validation in larger, sex-balanced external cohorts. Overall classification performance was modest; none of the evaluated classifiers demonstrated statistically significant above-chance performance in the permutation tests, and the fNIRS features did not demonstrate statistically significant incremental predictive value beyond age and sex.