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http://dx.doi.org/10.22156/CS4SMB.2019.9.8.248

Mild Cognitive Impairment Prediction Model of Elderly in Korea Using Restricted Boltzmann Machine  

Byeon, Haewon (Department of Speech Language Pathology, Honam University)
Publication Information
Journal of Convergence for Information Technology / v.9, no.8, 2019 , pp. 248-253 More about this Journal
Abstract
Early diagnosis of mild cognitive impairment (MCI) can reduce the incidence of dementia. This study developed the MCI prediction model for the elderly in Korea. The subjects of this study were 3,240 elderly (1,502 men, 1,738 women) aged 65 and over who participated in the Korean Longitudinal Survey of Aging (KLoSA) in 2012. Outcome variables were defined as MCI prevalence. Explanatory variables were age, marital status, education level, income level, smoking, drinking, regular exercise more than once a week, average participation time of social activities, subjective health, hypertension, diabetes Respectively. The prediction model was developed using Restricted Boltzmann Machine (RBM) neural network. As a result, age, sex, final education, subjective health, marital status, income level, smoking, drinking, regular exercise were significant predictors of MCI prediction model of rural elderly people in Korea using RBM neural network. Based on these results, it is required to develop a customized dementia prevention program considering the characteristics of high risk group of MCI.
Keywords
Neural network; Restricted boltzmann machine; Predictive model; Mild cognitive impairment; Risk factors;
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