신경망 모델을 이용한 치통발생 예측 모형에 관한 연구

Predictive Modeling of Dental Pain Factors Using Neural Network Model

  • 김은엽 (초당대학교 치위생학과) ;
  • 임근옥 (초당대학교 치위생학과)
  • Kim, Eun-Yeob (Department of Dental Hygiene, Chodang University) ;
  • Lim, Kun-Ok (Department of Dental Hygiene, Chodang University)
  • 투고 : 2009.05.12
  • 심사 : 2009.06.11
  • 발행 : 2009.06.30

초록

본 연구는 구조화된 설문을 통하여 구강건강 유지 및 증진을 위해 구강건강실태를 조사 분석하여 치통을 일으키는 요인을 기반으로 치통예측모형을 개발하였다(n=110). 1. 연구대상자는 총 110명 남성 27명, 여성 83명이었다. 신장 평균은 남성 172.59cm, 여성 161.95cm, 체중 평균은 남성 64.33kg, 여성 53.81kg이었다. BMI (Body Mass Index)는 남성이 $21.58{\pm}1.84$, 여성 $20.51{\pm}2.00$으로 성별에 따라 유의하였다(p=0.004). 2. 식습관0 조사 결과 선호하는 맞은 남성 51.8%가 짠맛을 선호하는 반면, 여성 62.7%는 보통(중간)의 맛을 선호하는 것으로 나타나 성별에 따른 차이가 나타났다(p=0.009). 본인의 식사가 균형이 있는지 인식을 조사한 결과 남성 76.9%는 '그렇다'라고 한 반면, 여성 49.4%만 균형 있는 식사를 하고 있다고 하여 성별에 따른 차이가 있었다(p = 0.011). 3. 운동 및 기호에 대한 조사결과 일주일 동안 운동 시간에 대한 결과 남성 55.6%, 여성 55.5%가 주당 4시간미만 운동하는 것으로 나타났다. 지금 운동의 적절성에 대한 인식 결과 남성 82.6%, 여성 66.7%가 적정한 운동이라고 답하였다. 흡연은 남성 77.8%, 여성 100%가 전혀 흡연을 한 경험이 없는 것으로 나타났다(p < 0.001). 4. 구강 건강 습관 조사결과 조사시점에 치통 유무는 남성 11.5%, 여성 20.7%가 있는 것으로 나타났다. 칫솔질 교육은 남성 55.6%, 여성 69.9%가 받았다고 하였다. 하루 3번 칫솔질하는 횟수는 남성 50.0%, 여성 66.3%로 나타났다. 5. 잇몸수술 경험은 없으며, 칫솔횟수는 하루 4회하며 균형 있는 식습관을 하고 있으며, 약간 단맛을 선호하는 사람이 치통을 더 느끼는 것으로 나타났다. 6. 치통 예측 모델링에 대한 결과 신경망 모델을 사용한 상대적 중요도가 높은 독립변수는 선호 맛, 스트레스 합, 흡연 유무, 잇몸수술, BMI, 균형 있는 식사 인식, 나이였으며, 치통발생 모형의 정확도는 88.75%이었다.

Oral diseases may hinder people from living a healthy life by causing obstacles in the nutrition supply of the human body. This study aims at the found out the eating habits and recognition factors of people who are currently suffering from denial pain, and made a predictive modeling using neural network, which is a data mining. The oral health condition for maintaining and improving oral health has been examined and analyzed through a survey and the groups were divided based on the presence and the absence of dental pain. This study observed on eating habits, exercise and oral habits. The study results of neural network modeling input parameter was selected significant survival factors. As a result of making a predictive modeling using the neural network, the fitness of the predictive modeling of dental pain factors was 88.7%. As for the people who are likely to experience dental pain predicted by the neural network model, preventive measures including proper eating habits, education on oral hygiene, and stress release must precede any dental treatment.

키워드

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