<p class="jove_content">Functional near-infrared spectroscopy data from two task paradigms were evaluated using seven machine-learning classifiers under fold-specific preprocessing and leave-one-out cross-validation; observed performance was modest and did not exceed chance in permutation testing.</p>

functional-near-infrared-spectroscopy-combined-with-machine-learning

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 ...

Institution - Affiliated Children's Hospital of Jiangnan University (Wuxi Children's Hospital)

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