한국 성인의 치주질환 예측을 위한 머신러닝 알고리즘 성능 평가 및 분석
Performance Evaluation and Analysis of Machine Learning Algorithms for Predicting Periodontal Disease in Korean Adults
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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.
Abstract
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.