
==== Front
Res Sq
ResearchSquare
Research Square
2693-5015
American Journal Experts

39184094
10.21203/rs.3.rs-4745684/v1
10.21203/rs.3.rs-4745684
preprint
1
Article
Pitfalls in using ML to predict cognitive function performance
Kuhles Gianna
Hamdan Sami
Heim Stefan
Eickhoff Simon
Patil Kaustubh R.
Camilleri Julia
Weis Susanne
17 8 2024
rs.3.rs-4745684https://creativecommons.org/licenses/by/4.0/ This work is licensed under a Creative Commons Attribution 4.0 International License, which allows reusers to distribute, remix, adapt, and build upon the material in any medium or format, so long as attribution is given to the creator. The license allows for commercial use.
https://www.researchsquare.com/article/rs-4745684/v1
nihpp-rs4745684v1.pdf
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.
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