Earticle

초록

영어

In this study, we aimed to compare and evaluate the diagnostic accuracy of acute coronary syndrome for each model to which machine learning algorithm was applied. The data of a total of 857 patients with acute coronary syndrome were divided into train dataset and test dataset at a ratio of 7 : 3, and learning was performed by setting 10 folds. The model used for learning is evaluated by accuracy, AUC, recall, precision, F1 score, and kappa after training with random forest, gradient boosting, and ada boosting, and the AUC-ROC curve, confusion matrix, and accuracy are compared through the final model generated. evaluated. As a result of the experiment, the performance of the random forest model was the best, and as a result of the AUC-ROC curve and confusion matrix verification, the prediction performance of STEMI was gradient boosting, and the random forest predicted NSTEMI and unstable angina the best. As for the accuracy using verification data, the random forest produced the best results with 100%. The prediction of acute coronary syndrome according to the model applying the machine learning algorithm was the best in the random forest. It is believed that the results of this study can be used as basic and evidence data for the application of machine learning algorithms to the health care field, and further research using ensemble techniques can improve efficiency.

한국어

본 연구에서는 머신러닝 알고리즘을 적용한 모델 별 급성관상동맥증후군 진단 정확도를 비교 평가하고자 한다. 급성 관상동맥증후군 총 857명의 데이터를 학습데이터와 검증데이터로 7 : 3 비율로 구분하고 10 fold로 설정하여 학습에 진행하였다. 학습에 사용된 모델은 random forest, gradient boosting, ada boosting로 학습 후 정확도, AUC, recall, precision, F1 score, kappa로 평가하고, 최종 생성된 모델을 통해 AUC-ROC 곡선과 혼동행렬, 정확도를 비교 평가 하였다. 실험 결과 random forest 모델의 성능이 가장 우수 하였으며, AUC-ROC곡선과 혼동행렬 검증 결과 STEMI 예측성능은 gradient boosting, NSTEMI와 불안정성협심증은 random forest가 가장 예측을 잘 하였다. 검증데이터를 이용한 정확도는 random forest가 100%로 가장 우수한 결과를 도출하였다. 머신러닝 알고리즘을 적용 한 모델에 따른 급성관상동맥증후군 예측은 random forest가 가장 우수하였다. 본 연구결과를 통해 머신러닝 알고리즘 의 보건의료분야 적용에 대한 기초 및 근거 자료로 활용 될 수 있을 것으로 사료되며, 추후 앙상블 기법을 이용한 추가 연구를 통해 효율성을 높일 수 있을 것으로 사료된다.

목차

Abstract
Ⅰ. 서론
Ⅱ. 재료 및 방법
1. 환자 자료
2. 라이브러리 (library)
3. 모델구축 및 평가방법
Ⅲ. 결과
Ⅳ. 고찰 및 결론
Ⅴ. 참고문헌
요약

키워드

저자

• Mu Seong Kim [ 김무성 | Department of Radiological science, College of Health Sciences, Eulji University ]
• Ye Ji Kwon [ 권예지 | Department of Radiological science, College of Health Sciences, Eulji University ]
• Ji Min Park [ 박지민 | Department of Radiological science, College of Health Sciences, Eulji University ]
• Hye Jeong Jeon [ 전혜정 | Department of Radiological science, College of Health Sciences, Eulji University ]
• Ji Hye Hong [ 홍지혜 | Department of Radiological science, College of Health Sciences, Eulji University ]
• Joo Wan Hong [ 홍주완 | Department of Radiological science, College of Health Sciences, Eulji University ] Corresponding Author

참고문헌

발행기관

간행물

표지보기 관심저널 등록

• 간행물명

  대한디지털의료영상학회논문지 [Korean Journal of Digital Imaging in Medicine]

• 간기

  연3회

• pISSN

  1976-7641

• 수록기간

  1995~2025

• 십진분류

  KDC 510 DDC 610