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

제한된 볼츠만 기계학습 알고리즘을 이용한 우리나라 지역사회 노인의 경도인지장애 예측모형

  • Byeon, Haewon (Department of Speech Language Pathology, Honam University)
  • 변해원 (호남대학교 보건과학대학 언어치료학과)
  • Received : 2019.07.08
  • Accepted : 2019.08.20
  • Published : 2019.08.28

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.

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Fig. 1. Concept of Restricted Boltzmann Machines [9]

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Fig. 2. Input path of Restricted Boltzmann Machines [9]

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Fig. 3. Weighted input path of Restricted Boltzmann Machines [9]

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Fig. 4. ROC curve of Restricted Boltzmann Machines

Table 1. Characteristics of the subjects based on MCI, n(%)

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Table 2. Relative importance of inputs

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Acknowledgement

Supported by : National Research Foundation of Korea(NRF)

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