Prediction of death status on the course of treatment in SARS-COV-2 patients with deep learning and machine learning methods


Kivrak M., GÜLDOĞAN E., ÇOLAK C.

COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE, vol.201, 2021 (SCI-Expanded) identifier identifier identifier

  • Publication Type: Article / Article
  • Volume: 201
  • Publication Date: 2021
  • Doi Number: 10.1016/j.cmpb.2021.105951
  • Journal Name: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Applied Science & Technology Source, BIOSIS, Biotechnology Research Abstracts, Compendex, Computer & Applied Sciences, EMBASE, INSPEC, MEDLINE
  • Keywords: SARS-COV-2, Data Mining, Deep Learning, Extreme Gradient Boosting, Machine Learning, WUHAN
  • Inonu University Affiliated: Yes

Abstract

Background and Objective: The new type of Coronavirus (2019-nCov) epidemic spread rapidly, causing more than 250 thousand deaths worldwide. The virus, which first appeared as a sign of pneumonia, was later called the SARS-COV-2 with Severe Acute Respiratory Syndrome by the World Health Organization. The SARS-COV-2 virus is triggered by binding to the Angiotensin-Converting Enzyme 2 (ACE 2) inhibitor, which is vital in cardiovascular diseases and the immune system, especially in conditions such as cerebrovascular, hypertension, and diabetes. This study aims to evaluate the prediction performance of death status based on the demographic/clinical factors (including COVID-19 severity) by data mining methods.