융합정보논문지 (Journal of Convergence for Information Technology)

중소기업융합학회 (Convergence Society for SMB)
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제한된 볼츠만 기계학습 알고리즘을 이용한 우리나라 지역사회 노인의 경도인지장애 예측모형

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

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

노인성 치매의 전 임상단계인 경도인지장애(MCI)를 조기 진단하고, 조기 개입한다면, 치매의 발병률을 줄일 수 있다. 본 연구는 우리나라 지역사회 노인의 MCI 예측 모형을 개발하고 노년기 인지장애의 예방을 위한 기초자료를 제공하였다. 연구대상은 2012년 Korean Longitudinal Survey of Aging(KLoSA)에 참여한 65세 이상 지역사회 노인 3,240명(남성 1,502명, 여성 1,738명)이다. 결과변수는 MCI유병으로 정의하였고, 설명변수는 성, 연령, 혼인상태, 교육수준, 소득수준, 흡연, 음주, 주1회 이상의 정기적인 운동, 월평균 사회활동 참여시간, 주관적 건강, 고혈압, 당뇨병을 포함하였다. 예측모형의 개발은 Restricted Boltzmann Machine(RBM) 인공신경망을 이용하였다. RMB 인공신경망을 이용하여 우리나라 지역사회 노인의 MCI 예측 모형을 구축한 결과, 유의미한 요인은 연령, 성별, 최종학력, 주관적 건강, 혼인상태, 소득수준, 흡연, 규칙적 운동이었다. 이 결과를 기초로 MCI 고위험군의 특성을 고려한 맞춤형 치매 예방 프로그램의 개발이 요구된다.

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

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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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