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Related Experiment Video

Updated: Feb 2, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
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Detection of cognitive impairment using a machine-learning algorithm.

Young Chul Youn1, Seong Hye Choi2, Hae-Won Shin1

  • 1Department of Neurology, College of Medicine, Chung-Ang University, Seoul, South Korea.

Neuropsychiatric Disease and Treatment
|November 23, 2018
PubMed
Summary

This study developed a machine learning algorithm to detect cognitive impairment (CI). The Mini-Mental State Examination (MMSE) algorithm showed superior accuracy, but the Korean Dementia Screening Questionnaire (KDSQ) offers a more practical screening tool.

Keywords:
Mini-Mental State ExaminationTensorFlowdementiadementia questionnairemachine learningmild cognitive impairment

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Area of Science:

  • Neurology
  • Artificial Intelligence
  • Gerontology

Background:

  • Cognitive impairment (CI) screening is crucial for timely intervention.
  • The Mini-Mental State Examination (MMSE) is widely used but can be time-consuming.
  • The Korean Dementia Screening Questionnaire (KDSQ) offers a potentially simpler alternative.

Purpose of the Study:

  • To develop and evaluate machine learning algorithms for CI detection.
  • To compare the performance of algorithms based on KDSQ, MMSE, and their combination.
  • To assess the clinical utility of KDSQ and MMSE in CI screening.

Main Methods:

  • Utilized a dataset of 9,885 patients for training and 300 for testing.
  • Selected up to 24 clinical variables, including demographics and comorbidities.
  • Trained a machine learning model using TensorFlow and logistic regression for cost analysis.

Main Results:

  • The MMSE-based algorithm achieved the highest accuracy (88.3%).
  • The KDSQ-based algorithm showed comparable performance (84.3% accuracy, 91.5% sensitivity).
  • Combining KDSQ and MMSE resulted in 86.3% accuracy and improved specificity (61.5%).

Conclusions:

  • The MMSE algorithm demonstrates superior predictive accuracy for CI.
  • The KDSQ algorithm provides a practical and comparable screening tool for clinical settings.
  • Machine learning enhances the diagnostic capabilities of cognitive screening tools.