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, cilt.201, 2021 (SCI-Expanded) identifier identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 201
  • Basım Tarihi: 2021
  • Doi Numarası: 10.1016/j.cmpb.2021.105951
  • Dergi Adı: COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
  • Derginin Tarandığı İndeksler: 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
  • Anahtar Kelimeler: SARS-COV-2, Data Mining, Deep Learning, Extreme Gradient Boosting, Machine Learning, WUHAN
  • İnönü Üniversitesi Adresli: Evet

Özet

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.