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Related Experiment Video

Updated: Sep 5, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Binary classification threatens the validity of cognitive impairment detection.

Maryse J Luijendijk1, Heleen E M Feenstra1, Ivar E Vermeulen2

  • 1Division of Psychosocial Research and Epidemiology.

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Summary

Binary classification of cognitive impairment shows poor agreement between similar test batteries, even when overall impairment rates are comparable. This highlights challenges in accurately identifying individuals with cognitive deficits in cancer patients.

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Area of Science:

  • Neuropsychology
  • Oncology
  • Cognitive Assessment

Background:

  • Cognitive impairment prevalence varies in patient populations despite standardized testing.
  • The International Cognition and Cancer Task Force (ICCTF) proposed standard cutoffs for cognitive impairment in cancer patients.
  • Harmonizing operationalization of cognitive impairment is crucial for accurate assessment.

Purpose of the Study:

  • To evaluate how binary classification of cognitive impairment affects agreement between two comparable test batteries.
  • To assess the impact of ICCTF criteria on cognitive impairment classification in cancer patients.
  • To investigate the agreement between traditional and online neuropsychological test formats.

Main Methods:

  • Two hundred non-central nervous system (non-CNS) cancer patients completed traditional and online test batteries.
  • Cognitive impairment was defined using ICCTF criteria (≥ 1.5 or ≥ 2 standard deviations below normative means).
  • Agreement was assessed using Cohen's kappa (κ), with additional Monte Carlo simulations.

Main Results:

  • Total scores between traditional and online tests showed a high correlation (.78).
  • Proportions of impaired patients were similar across methods (40% traditional vs. 38% online).
  • Within-person agreement in impairment classification was only fair (κ = .35), with low agreement in simulations (κ = .33–.41).

Conclusions:

  • Binary classification can lead to dissimilar impairment identification between highly similar test batteries.
  • Inherent low within-person agreement exists between assessment methods using binary classification.
  • Modern statistical tools may enhance the validity of cognitive impairment detection.