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Updated: Apr 18, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
[Polynesian adaptation of the Mini-Mental State Examination]
1Centre hospitalier de Polynésie française, avenue du Général-de-Gaulle, 50484 Pirae, Tahiti, Polynésie française.
Abstract:
This study aimed to develop and validate a Polynesian version of the MMSE (Mini-Mental State Examination). For this study a sample (n=112) of healthy people were evaluated with the French version of the consensual version of the MMSE, to target and modify some inadequate items for French Polynesia. Subsequently, a second sample (n=112) with the same characteristics (age, educational level) as well as 46 healthy people aged 60 years and more were evaluated with the adaptive version of the MMSE (P-MMSE). This version was then applied to 17 participants with Alzheimer disease. The control subjects were selected according to their age and educational level. The variables gender and evaluation sites were checked. An analysis of the results showed a significant dissociation between the two versions as well as a meaningful effect on global performance of the variables age (r=-0.45) and educational level (r=-0.25). Cut-off scores taking into consideration these variables were defined. The sensitivity and specificity values of the new cut-off scores were much greater than 0.5. Various global cut-off scores were also analyzed. A general cut-off score (≤23) was defined and yielded 82 % sensitivity and 75 % specificity in detecting Alzheimer disease.
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