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Pitfalls in using ML to predict cognitive function performance
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DOI：10.1038/s41598-025-24325-9.png)
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Abstract
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Machine learning analyses are widely used for predicting cognitive abilities, yet there are pitfalls that need to be considered during their implementation and interpretation of the results. Hence, the present study aimed at drawing attention to the risks of erroneous conclusions incurred by confounding variables illustrated by a case example predicting executive function (EF) performance by prosodic features. Healthy participants (n = 231) performed speech tasks and EF tests. From 264 prosodic features, we predicted EF performance using 66 variables, controlling for confounding effects of age, sex, and education. A reasonable prediction performance was apparently achieved for EF variables of the Trail Making Test. However, in-depth analyses revealed indications of confound leakage, leading to inflated prediction accuracies, due to a strong relationship between confounds and targets. These findings highlight the need to control confounding variables in ML pipelines and caution against potential pitfalls in ML predictions.
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Predicting the progression of mild cognitive impairment using machine learning: A systematic, quantitative and critical review.png)
MEDICAL IMAGE ANALYSIS
OAAI
MEDICAL IMAGE ANALYSISIF11.8
OAAIShareSaveHeterogeneity of executive function revealed by a functional random forest approach across ADHD and ASDNEUROIMAGE-CLINICALOAAI
NEUROIMAGE-CLINICALIF3.6
OAAIShareSaveSpEx: a German-language dataset of speech and executive function performanceSCIENTIFIC REPORTSOAAI
SCIENTIFIC REPORTSIF3.9
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