Exploring the Machine Learning Paradigm in Determining Risk for Reading Disability.

Florina ErbeliKai HeConnor CheekMarianne Rice Xiaoning Qian

Published in: Scientific studies of reading : the official journal of the Society for the Scientific Study of Reading (2022)

Findings suggest that RF does not outperform LR in RD prediction accuracy in models with multiple linearly related predictors. Findings also highlight including reading fluency in early identification batteries for later RD determination.

Keyphrases

machine learning
working memory
multiple sclerosis
artificial intelligence
molecularly imprinted
mass spectrometry
high resolution
drug induced
breast cancer risk