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A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
KAZAKH ADAPTATION OF THE MONTREAL COGNITIVE ASSESSMENT (MOCA)
A Utegenova1, A Utepkaliyeva1, G Kabdrakhmanova1
1West Kazakhstan Marat Ospanov Medical University, Aktobe, Kazakhstan.
Abstract:
The Montreal Cognitive Assessment (MoCA) is a brief cognitive evaluation tool that has been developed for screening of patients for Mild Cognitive Impairment (MCI). MCI is a recognized high-risk state for Alzheimer's disease development. The aim of the present study was to create a Kazakh-language adaptation of the original version of the Montreal Cognitive Assessment (version 7.1) and evaluate its reliability by determining internal consistency using the Cronbach's alpha coefficient. This prospective study involved 50 patients diagnosed with Parkinson's disease in accordance with the 2015 MDS clinical criteria with diagnosed MCI according to clinical guidelines of the Movement disorder society (MDS). Clinical and neuropsychological evaluation were carried out on all patients. The internal consistency and reliability of the translated scale were investigated by means of the Cronbach alpha coefficient. The Cronbach's alpha coefficient for the MoCA Kazakh version was 0.77. While the evaluation of discriminatory validity was not performed in this study, the Kazakh adaptation of the MoCA was shown to be a reliable tool for screening MCI among patients with Parkinson's Disease.

