Prediction of disorders with significant coronary lesions using machine learning in patients admitted with chest symptom.

Journal: PloS one
PMID: 36215242

Abstract

BACKGROUND: The early prediction of significant coronary artery lesion, including coronary vasospasm, have yet to be studied. It is essential to discern the disorders with significant coronary lesions (SCDs) requiring coronary angiography from mimicking disease. We aimed to determine which of all clinical variables were more important using conventional logistic regression (cLR) and machine learning (ML).

Authors

  • Jae Young Choi
    Image and Video Systems Laboratory, Department of Electrical Engineering, Korea Advanced Institute of Science and Technology (KAIST), Yuseong-Gu, Daejeon 305-701, Republic of Korea; Department of Biomedical Engineering, Jungwon University, 85 Munmu-Ro Goesan-Eup Goesan-Gun, Chungcheongbuk-Do 367-805, Republic of Korea.
  • Jae Hoon Lee
    Department of Food Science and Biotechnology of Animal Resources, Konkuk University, Seoul 05029, Korea.
  • Yuri Choi
    Department of Emergency Medicine, Dong-A University College of Medicine, Busan, Korea.
  • YunKyong Hyon
    Division of Medical Mathematics, National Institute for Mathematical Sciences, Daejeon, 34047, Republic of Korea.
  • Yong Hwan Kim
    Department of Emergency Medicine, Samsung Changwon Hospital, Sungkyunkwan University School of Medicine, Changwon, Korea.

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