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Updated: Aug 9, 2025

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
Published on: January 11, 2020
A New Smart 2-Min Mobile Alerting Method for Mild Cognitive Impairment Due to Alzheimer's Disease in the Community
Yujia Wang1,2, Tong Chen3, Chen Wang1,2
1School of Medical Technology and Information Engineering, Zhejiang Chinese Medical University, Hangzhou 310053, China.
Abstract:
The early identification of mild cognitive impairment (MCI) due to Alzheimer's disease (AD), in an early stage of AD can expand the AD warning window. We propose a new capability index evaluating the spatial execution process (SEP), which can dynamically evaluate the execution process in the space navigation task. The hypothesis is proposed that there are neurobehavioral differences between normal cognitive (NC) elderly and AD patients with MCI reflected in digital biomarkers captured during SEP. According to this, we designed a new smart 2-min mobile alerting method for MCI due to AD, for community screening. Two digital biomarkers, total mission execution distance (METRtotal) and execution distance above the transverse obstacle (EDabove), were selected by step-up regression analysis. For the participants with more than 9 years of education, the alerting efficiency of the combination of the two digital biomarkers for MCI due to AD could reach 0.83. This method has the advantages of fast speed, high alerting efficiency, low cost and high intelligence and thus has a high application value for community screening in developing countries. It also provides a new intelligent alerting approach based on the human-computer interaction (HCI) paradigm for MCI due to AD in community screening.
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