Predicting angiographic coronary artery disease using machine learning and high-frequency QRS.

Journal: BMC medical informatics and decision making
PMID: 39085823

Abstract

AIM: Exercise stress ECG is a common diagnostic test for stable coronary artery disease, but its sensitivity and specificity need to be further improved. In this paper, we construct a machine learning model for the prediction of angiographic coronary artery disease by HFQRS analysis of cycling exercise ECG.

Authors

  • Jiajia Zhang
    Hunan Provincial Key Laboratory of Clinical Epidemiology, Xiang Ya School of Public Health, Central South University, Changsha 410078, China.
  • Heng Zhang
    Department of Gastroenterology, The Central Hospital of Wuhan, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China.
  • Ting Wei
    Department of Bioinformatics and Biostatistics, School of Life Sciences and Biotechnology, Shanghai Jiao Tong University, Shanghai, China.
  • Pinfang Kang
    Department of Cardiovascular Disease, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233099, China.
  • Bi Tang
    Department of Cardiovascular Disease, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233099, China.
  • Hongju Wang
    Department of Cardiovascular Disease, The First Affiliated Hospital of Bengbu Medical University, Bengbu, Anhui Province, 233099, China. docwhj1101@163.com.

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