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Updated: Mar 24, 2026
A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
Post-hoc principal component analysis on a largely illiterate elderly population from North-west India to identify
Sunil Kumar Raina1, Vishav Chander1, Sujeet Raina2
1Department of Community Medicine, Dr. R. P. Government Medical College, Tanda, Himachal Pradesh, India.
Background:
Mini-mental state examination (MMSE) scale measures cognition using specific elements that can be isolated, defined, and subsequently measured. This study was conducted with the aim to analyze the factorial structure of MMSE in a largely, illiterate, elderly population in India and to reduce the number of variables to a few meaningful and interpretable combinations.
Methodology:
Principal component analysis (PCA) was performed post-hoc on the data generated by a research project conducted to estimate the prevalence of dementia in four geographically defined habitations in Himachal Pradesh state of India.
Results:
Questions on orientation and registration account for high percentage of cumulative variance in comparison to other questions.
Discussion:
The PCA conducted on the data derived from a largely, illiterate population reveals that the most important components to consider for the estimation of cognitive impairment in illiterate Indian population are temporal orientation, spatial orientation, and immediate memory.
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