PMID- 37064314
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230418
IS  - 2398-8835 (Electronic)
IS  - 2398-8835 (Linking)
VI  - 6
IP  - 4
DP  - 2023 Apr
TI  - Investigating the performance of machine learning algorithms in predicting the 
      survival of COVID-19 patients: A cross section study of Iran.
PG  - e1212
LID - 10.1002/hsr2.1212 [doi]
LID - e1212
AB  - BACKGROUND AND AIMS: Like early diagnosis, predicting the survival of patients 
      with Coronavirus Disease 2019 (COVID-19) is of great importance. Survival 
      prediction models help doctors be more cautious to treat the patients who are at 
      high risk of dying because of medical conditions. This study aims to predict the 
      survival of hospitalized patients with COVID-19 by comparing the accuracy of 
      machine learning (ML) models. METHODS: It is a cross-sectional study which was 
      performed in 2022 in Fasa city in Iran country. The research data set was 
      extracted from the period February 18, 2020 to February 10, 2021, and contains 
      2442 hospitalized patients' records with 84 features. A comparison was made 
      between the efficiency of five ML algorithms to predict survival, includes Naive 
      Bayes (NB), K-nearest neighbors (KNN), random forest (RF), decision tree (DT), 
      and multilayer perceptron (MLP). Modeling steps were done with Python language in 
      the Anaconda Navigator 3 environment. RESULTS: Our findings show that NB 
      algorithm had better performance than others with accuracy, precision, recall, 
      F-score, and area under receiver operating characteristic curve of 97%, 96%, 96%, 
      96%, and 97%, respectively. Based on the analysis of factors affecting survival, 
      heart disease, pulmonary diseases and blood related disease were the most 
      important disease related to death. CONCLUSION: The development of software 
      systems based on NB will be effective to predict the survival of COVID-19 
      patients.
CI  - (c) 2023 The Authors. Health Science Reports published by Wiley Periodicals LLC.
FAU - Yazdani, Azita
AU  - Yazdani A
AUID- ORCID: 0000-0002-5190-286X
AD  - Department of Health Information Management, School of Health Management and 
      Information Sciences Shiraz University of Medical Sciences Shiraz Iran.
AD  - Clinical Education Research Center Shiraz University of Medical Sciences Shiraz 
      Iran.
AD  - Health Human Resources Research Center, School of Health Management and 
      Information Sciences Shiraz University of Medical Sciences Shiraz Iran.
FAU - Bigdeli, Somayeh Kianian
AU  - Bigdeli SK
AUID- ORCID: 0009-0008-0278-3033
AD  - Health Information Management Department, School of Allied Medical Sciences 
      Tehran University of Medical Sciences Tehran Iran.
FAU - Zahmatkeshan, Maryam
AU  - Zahmatkeshan M
AUID- ORCID: 0000-0003-4090-391X
AD  - Noncommunicable Diseases Research Center Fasa University of Medical Sciences Fasa 
      Iran.
AD  - School of Allied Medical Sciences Fasa University of Medical Sciences Fasa Iran.
LA  - eng
PT  - Journal Article
DEP - 20230413
PL  - United States
TA  - Health Sci Rep
JT  - Health science reports
JID - 101728855
PMC - PMC10099201
OTO - NOTNLM
OT  - COVID-19
OT  - K-nearest neighbors
OT  - Naive Bayes
OT  - decision tree
OT  - machine learning
OT  - random forest
COIS- The authors declare no conflict of interest.
EDAT- 2023/04/18 06:00
MHDA- 2023/04/18 06:01
PMCR- 2023/04/13
CRDT- 2023/04/17 03:41
PHST- 2022/09/10 00:00 [received]
PHST- 2023/03/23 00:00 [revised]
PHST- 2023/03/30 00:00 [accepted]
PHST- 2023/04/18 06:01 [medline]
PHST- 2023/04/17 03:41 [entrez]
PHST- 2023/04/18 06:00 [pubmed]
PHST- 2023/04/13 00:00 [pmc-release]
AID - HSR21212 [pii]
AID - 10.1002/hsr2.1212 [doi]
PST - epublish
SO  - Health Sci Rep. 2023 Apr 13;6(4):e1212. doi: 10.1002/hsr2.1212. eCollection 2023 
      Apr.