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Updated: Jun 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

Published on: January 11, 2020

Multiple imputation was an efficient method for harmonizing the Mini-Mental State Examination with missing item-level

Richard A Burns¹, Peter Butterworth, Kim M Kiely

¹Ageing Research Unit, Centre for Mental Health Research, Australian National University, Canberra, ACT, Australia. richard.burns@anu.edu.au

Journal of Clinical Epidemiology

|February 5, 2011

Summary

Multiple imputation (MI) effectively estimates missing Mini-Mental State Examination (MMSE) data for cognitive assessment. However, accuracy decreases significantly when over 50% of item-level data is missing, especially in older adults.

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Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

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Last Updated: Jun 4, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment

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Published on: January 11, 2020

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Multimedia Battery for Assessment of Cognitive and Basic Skills in Mathematics (BM-PROMA)

Published on: August 28, 2021

Area of Science:

Gerontology
Cognitive Science
Biostatistics

Background:

The Mini-Mental State Examination (MMSE) is crucial for assessing cognitive function and screening for dementia.
Missing data in MMSE assessments is common, and traditional methods like listwise deletion or mean imputation may be insufficient.
Accurate handling of missing MMSE data is vital for reliable cognitive status estimation.

Purpose of the Study:

To evaluate the efficacy of multiple imputation (MI) for estimating missing item-level data in the MMSE.
To assess the impact of the proportion of missing data on the accuracy of MI in MMSE scores.
To determine the suitability of MI for handling missing MMSE data across different demographic groups.

Main Methods:

Utilized multiple imputation (MI) techniques to address missing MMSE item-level data.
Analyzed data from 17,303 participants from the Dynamic Analyses to Optimize Aging project.
Employed a simulation model to test the performance of MI under varying missing data conditions.

Main Results:

Found significant differences in mean MMSE scores between participants with and without missing data, consistent across age and gender.
MI generally inflated MMSE scores, though differences between imputed and complete data persisted.
MI demonstrated efficacy in estimating missing item-level data, but accuracy declined sharply when 50% or more data were missing, particularly in older individuals.

Conclusions:

The adapted multiple imputation method offers a viable approach for estimating missing MMSE item-level data when missingness is not excessive.
Careful consideration of the proportion of missing data is necessary when applying MI to MMSE assessments.
This method provides a more robust alternative to traditional techniques for incomplete MMSE datasets.