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

서지정보

ㆍ발행기관 : 충북대학교 동물의학연구소 ㆍ수록지정보 : Journal of Biomedical and Translational Research / 23권 / 1호
ㆍ저자명 : Ho Sun Shon, Eun Sun Choi, Yan-Sub Cho, Eun Jong Cha, Tae-Geon Kang, Kyung Ah Kim

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

영어 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.

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