건강설문조사 데이터를 활용한 인공지능 알고리즘 기반 여성의 골관절염 및 골다공증 진단 예측 모델 개발

다국어 초록 (Multilingual Abstract)

Although osteoporosis and osteoarthritis have different symptoms, there is the common feature that both have high prevalence rate among adult women. However, it is challenging to receive a cost-effective and consistent diagnosis for bone-related diseases, since it is expensive to take MRI or CT examinations of osteoporosis and the measurement instruments for osteoarthritis provide divergent criteria for bone density. In this study, we propose a predictive model for diagnosing osteoarthritis and osteoporosis in women based on Artificial Intelligence algorithms using health survey data. Three Artificial Intelligence algorithms, such as Logistic Regression, Random Forest Classifier, and eXtreme Gradient Boosting machine, are considered for our provided prediction models. Because the health survey data we used is imbalanced, under-sampling technique was applied to improve the model’s performance. In addition, various feature sets were selected to reduce the dimensionality of independent variables. We identified that the prediction model based on the eXtreme Gradient Boosting machine algorithm, which uses the dataset applied by under-sampling method, exhibits the best performance.

목차 (Table of Contents)

• 요약
• Ⅰ. 서론
• Ⅱ. 데이터 세트 설명 및 전처리 과정
• Ⅲ. 알고리즘 및 성능 평가 지표
• Ⅳ. 골관절염 진단 예측 모델 분석
• Ⅴ. 골다공증 진단 예측 모델 분석
• Ⅵ. 결론
• 참고문헌