A soft voting ensemble classifier for early prediction and diagnosis of occurrences of major adverse cardiovascular events for STEMI and NSTEMI during 2-year follow-up in patients with acute coronary syndrome.

Journal: PloS one
PMID: 34115750

Abstract

OBJECTIVE: Some researchers have studied about early prediction and diagnosis of major adverse cardiovascular events (MACE), but their accuracies were not high. Therefore, this paper proposes a soft voting ensemble classifier (SVE) using machine learning (ML) algorithms.

Authors

  • Syed Waseem Abbas Sherazi
    Department of Computer Science, Chungbuk National University, Cheongju, Chungbuk, South Korea.
  • Jang-Whan Bae
    Department of Internal Medicine, College of Medicine, Chungbuk National University, Cheongju, Chungbuk, South Korea.
  • Jong Yun Lee
    Database and Bioinformatics Laboratory, School of Electrical and Computer Engineering, Chungbuk National University, Cheongju, Korea.

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