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Machine learning-based risk factor analysis for periodontal disease from a Korean National Survey KCI 등재

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  • URLhttps://db.koreascholar.com/Article/Detail/413321
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충북대학교 동물의학연구소 (Research Institute of Veterinary Medicine, Chungbuk National University)
초록

Periodontal disease is a chronic but treatable condition which often does not cause pain during the initial stages of the illness. Lack of awareness of symptoms can delay initiation of treatment and worsen health. The aim of this study was to develop and compare different risk prediction models for periodontal disease using machine learning algorithms. We obtained information on risk factors for periodontal disease from the Korea National Health and Nutrition Examination Survey (KNHANES) dataset. Principal component analysis and an auto-encoder were used to extract data on risk factors for periodontal disease. A synthetic minority oversampling technique algorithm was used to solve the problem of data imbalance. We used a combination of logistic regression analysis, support vector machine (SVM) learning, random forest, and AdaBoost to classify and compare risk prediction models for periodontal disease. In cases where we used principal component analysis (PCA) to extract risk factors, the recall was higher than the feature selection method in the logistic regression and support-vector machine learning models. AdaBoost’s recall was 0.98, showing the highest performance of both feature selection and PCA. The F1 score showed relatively high performance in Ada- Boost, logistic regression, and SVM learning models. By using the risk factors extracted from the research results and the predictive model based on machine learning, it will be able to help in the prevention and diagnosis of periodontal disease, and it will be used to study the relationship with various diseases related to periodontal disease.

목차
Abstract
INTRODUCTION
MATERIALS AND METHODS
    Material
    Feature selection
    Analysis method
RESULTS
    Characteristics of risk factors for periodontal disease
    Performance comparison of classification model
DISCUSSION
REFERENCES
저자
  • Ho Sun Shon(Medical Research Institute, School of Medicine, Chungbuk National University)
  • Eun Sun Choi(Department of Big Data Cooperative Course, Chungbuk National University)
  • Yan-Sub Cho(Department of Management Information Systems, Chungbuk National University)
  • Eun Jong Cha(Department of Biomedical Engineering, School of Medicine, Chungbuk National University)
  • Tae-Geon Kang(Institute for Trauma Research, College of Medicine, Korea University)
  • Kyung Ah Kim(Department of Biomedical Engineering, School of Medicine, Chungbuk National University) Corresponding author