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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Designing an effective semantic fluency test for early MCI diagnosis with machine learning.

Alba Gómez-Valadés1, Rafael Martínez1, Mariano Rincón1

  • 1Universidad Nacional de Educación a Distancia, Madrid, 28040, Comunidad Autónoma de Madrid, Spain(1).

Computers in Biology and Medicine
|August 17, 2024
PubMed
Summary

This study identifies the most efficient semantic fluency tests for early Mild Cognitive Impairment (MCI) detection. Combining animal and clothing categories with specific variables significantly reduces testing time while maintaining diagnostic accuracy.

Keywords:
ClusteringEarly diagnosisMCIMachine learningSemantic fluency testSwitching

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Area of Science:

  • Neuroscience
  • Cognitive Psychology
  • Machine Learning

Background:

  • Semantic fluency tests are crucial for early Mild Cognitive Impairment (MCI) detection.
  • Speech and semantic memory impairments are early MCI symptoms.
  • Existing tests can be time-consuming, necessitating more efficient diagnostic tools.

Purpose of the Study:

  • To identify the minimal yet effective combination of semantic categories and variables for MCI diagnosis.
  • To reduce the duration of semantic fluency testing batteries.
  • To maintain or enhance diagnostic accuracy while improving efficiency.

Main Methods:

  • Utilized machine learning algorithms.
  • Analyzed a database of 423 assessments from 141 subjects over three time points.
  • Categorized subjects into Healthy, stable MCI, and heterogeneous MCI groups.

Main Results:

  • The most efficient combination included the 'animals' and 'clothes' semantic categories.
  • Key variables identified were 'corrects', 'switching', 'clustering', and 'total clusters'.
  • This combination effectively distinguished between diagnostic groups.

Conclusions:

  • A reduced set of semantic fluency tests (animals, clothes categories; corrects, switching, clustering, total clusters variables) offers an efficient diagnostic approach.
  • This optimized combination balances diagnostic capability with time efficiency.
  • Ideal for large-scale screenings and clinical settings prioritizing speed.