% IMPORTANT: The following is UTF-8 encoded.  This means that in the presence
% of non-ASCII characters, it will not work with BibTeX 0.99 or older.
% Instead, you should use an up-to-date BibTeX implementation like “bibtex8” or
% “biber”.

@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},
}