한국 성인의 치주질환 예측을 위한 머신러닝 알고리즘 성능 평가 및 분석

• Published in: 보건정보통계학회지, 50(2) pp. 163 - 171
• Main Authors: 정은서, 최기봉, 김혜영
• Format: Journal Article
• Language: English
• Published: 한국보건정보통계학회 01.05.2025
• Subjects: 예방의학
• ISSN: 2465-8014; 2465-8022

Objectives: This study aimed to enhance the accuracy of predicting periodontal disease using machine learning algorithms and to identify key risk fac- tors essential for developing personalized prevention and management strategies. Methods: Data from 11,781 adults aged 19 years or older were obtained from the 7th Korea National Health and Nutrition Examination Survey (2016–2018). Five machine learning algorithms, including logistic regression, deci- sion tree, random forest, extreme gradient boosting, and CatBoost, were applied. Models were trained and evaluated using a complex sampling design and 10-fold cross-validation. Results: The prevalence of periodontal disease was 27.8%. The CatBoost model demonstrated the highest predictive per- formance (AUC: 0.760). Age, sex, and education level were identified as key predictors, significantly influencing model accuracy. Conclusions: This study highlights the potential of machine learning-based prediction models in the early detection of periodontal disease and the development of personalized prevention strategies. KCI Citation Count: 0