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@ARTICLE{Kuhles:1033912,
author = {Kuhles, Gianna and Hamdan, Sami and Heim, Stefan and
Eickhoff, Simon and Patil, Kaustubh and Camilleri, Julia and
Weis, Susanne},
title = {{P}itfalls in using {ML} to predict cognitive function
performance},
journal = {Research Square},
reportid = {FZJ-2024-06750},
year = {2024},
abstract = {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 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
model fit 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.},
cin = {INM-1 / INM-7},
cid = {I:(DE-Juel1)INM-1-20090406 / I:(DE-Juel1)INM-7-20090406},
pnm = {5254 - Neuroscientific Data Analytics and AI (POF4-525) /
JL SMHB - Joint Lab Supercomputing and Modeling for the
Human Brain (JL SMHB-2021-2027)},
pid = {G:(DE-HGF)POF4-5254 / G:(DE-Juel1)JL SMHB-2021-2027},
typ = {PUB:(DE-HGF)25},
doi = {10.21203/rs.3.rs-4745684/v1},
url = {https://juser.fz-juelich.de/record/1033912},
}