폐경 여성에서 트리기반 머신러닝 모델로부터 골다공증 예측

• 영문명: Predictive of Osteoporosis by Tree-based Machine Learning Model in Post-menopause Woman
• 발행기관: 대한방사선과학회(구 대한방사선기술학회)
• 저자명: 이인자(Lee In-Ja) 이준호(Lee Junho)
• 간행물 정보: 『방사선기술과학』방사선기술과학 제43권 제6호, 495~502쪽, 전체 8쪽
• 주제분류: 의약학 > 방사선과학
• 파일형태: PDF
• 발행일자: 2020.12.31

영문 초록

In this study, the prevalence of osteoporosis was predicted based on 10 independent variables such as age, weight, and alcohol consumption and 4 tree-based machine-learning models, and the performance of each model was compared. Also the model with the highest performance was used to check the performance by clearing the independent variable, and Area Under Curve(ACU) was utilized to evaluate the performance of the model. The ACU for each model was Decision tree 0.663, Random forest 0.704, GBM 0.702, and XGBoost 0.710 and the importance of the variable was shown in the order of age, weight, and family history. As a result of using XGBoost, the highest performance model and clearing independent variables, the ACU shows the best performance of 0.750 with 7 independent variables. This data suggests that this method be applied to predict osteoporosis, but also other various diseases. In addition, it is expected to be used as basic data for big data research in the health care field.

목차

Ⅰ. 서 론

Ⅱ. 대상 및 방법

Ⅲ. 결 과

Ⅳ. 고 찰

Ⅴ. 결 론

REFERENCES

키워드

골다공증 골밀도 머신러닝 예측모델 Osteoporosis Bone mineral density Machine learning XGBoost Prediction model