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베이지안 네트워크를 활용한 치매 의심군 예측요인 연구

A Study on the Prediction of Questionable Dementia Using Bayesian Network

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초록

연구목적: 본 연구는 중․장년층의 치매 의심군 선별을 위하여 예측요인을 확인하고 중․장년층의 치매 예방과 조기 맞춤형 개입 방안을 마련하기 위한 기초자료를 제공하고자 한다. 연구방법: 전국에 거주하는 40∼65세 중․장년층 850명을 대상으로 설문조사를 실시하여 자료를 수집하고 베이지안 네트워크를 활용하여 치매 의심군을 선별하는 예측요인과 경로를 확인하였다. 연구결과: 첫째, 베이지안 네트워크 모델로 추출된 결과값의 정확성을 검증하기 위하여, 랜덤 포레스트, 그래디언트 부스팅과 성능을 비교하였으며 그 결과, 베이지안 네트워크 알고리즘의 성능이 가장 우수하였으며, 그 예측력은 90%로 나타났다. 둘째, 치매 의심군을 예측하는 변수 중 가장 예측력이 큰 변수는 주관적 기억저하이며, 다음으로 초기부적응도식, 사회적 편안함, 성인애착불안, 치매두려움, 성인애착회피 순으로 나타났다. 셋째, 치매 의심군을 나타내는 핵심적 경로는 신경증에서 초기부적응도식과 성인애착불안을 거쳐 치매 의심군으로 이어지는 경로이며, 다음으로 신경증에서 주관적 기억저하, 사회적 편안함, 성인애착불안을 거쳐 치매 의심군으로 이어지는 경로가 확인되었다. 결론: 베이지안 네트워크를 활용하여 치매 의심군의 중요변수와 변수 간 경로를 살펴봄으로써, 치매에 대한 조기개입 및 맞춤형 정책 마련을 위한 조망의 틀을 제공할 수 있을 것이다.

Abstract

Objective: This study aims to identify predictive factors for screening suspected dementia among middle-aged and older adults and provide basic data to prevent dementia and prepare early customized intervention plans for middle-aged and older adults. Method: A survey of 850 middle-aged and older adults aged 40-65 years living in Japan was conducted to collect data, and a Bayesian network was used to identify predictors and pathways for screening suspected dementia. Results: First, to verify the accuracy of the results extracted by the Bayesian network model, the performance was compared with random forest and gradient boosting, and the results showed that the Bayesian network algorithm performed the best, with a predictive power of 90%. Second, among the variables predicting suspected dementia, the most predictive variable was subjective memory decline, followed by early maladaptive behavior, social comfort, adult attachment anxiety, dementia fear, and adult attachment avoidance. Third, the core pathway for suspected dementia was identified as neuroticism to early maladaptive behavior and adult attachment anxiety to suspected dementia, followed by neuroticism to subjective memory decline, social comfort, and adult attachment anxiety to suspected dementia. Conclusion: By utilizing Bayesian networks to explore important variables and pathways between variables in suspected dementia, we can provide a framework for early intervention and customized policies for dementia.

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