PMID- 35673548
OWN - NLM
STAT- PubMed-not-MEDLINE
LR  - 20230207
IS  - 1959-0318 (Print)
IS  - 1876-0988 (Electronic)
IS  - 1876-0988 (Linking)
VI  - 44
IP  - 1
DP  - 2023 Feb
TI  - Automatic Detection of Severely and Mildly Infected COVID-19 Patients with 
      Supervised Machine Learning Models.
PG  - 100725
LID - 10.1016/j.irbm.2022.05.006 [doi]
AB  - OBJECTIVES: When the prognosis of COVID-19 disease can be detected early, the 
      intense-pressure and loss of workforce in health-services can be partially 
      reduced. The primary-purpose of this article is to determine the feature-dataset 
      consisting of the routine-blood-values (RBV) and demographic-data that affect the 
      prognosis of COVID-19. Second, by applying the feature-dataset to the supervised 
      machine-learning (ML) models, it is to identify severely and mildly infected 
      COVID-19 patients at the time of admission. MATERIAL AND METHODS: The sample of 
      this study consists of severely (n = 192) and mildly (n = 4010) infected-patients 
      hospitalized with the diagnosis of COVID-19 between March-September, 2021. The 
      RBV-data measured at the time of admission and age-gender characteristics of 
      these patients were analyzed retrospectively. For the selection of the features, 
      the minimum-redundancy-maximum-relevance (MRMR) method, 
      principal-components-analysis and forward-multiple-logistics-regression analyzes 
      were used. The features set were statistically compared between mild and severe 
      infected-patients. Then, the performances of various supervised-ML-models were 
      compared in identifying severely and mildly infected-patients using the feature 
      set. RESULTS: In this study, 28 RBV-parameters and age-variable were found as the 
      feature-dataset. The effect of features on the prognosis of the disease has been 
      clinically proven. The ML-models with the highest overall-accuracy in identifying 
      patient-groups were found respectively, as follows: local-weighted-learning 
      (LWL)-97.86%, K-star (K*)-96.31%, Naive-Bayes (NB)-95.36% and k-nearest-neighbor 
      (KNN)-94.05%. Also, the most successful models with the highest 
      area-under-the-receiver-operating-characteristic-curve (AUC) values in 
      identifying patient groups were found respectively, as follows: LWL-0.95%, 
      K*-0.91%, NB-0.85% and KNN-0.75%. CONCLUSION: The findings in this article have 
      significant a motivation for the healthcare professionals to detect at admission 
      severely and mildly infected COVID-19 patients.
CI  - (c) 2022 AGBM. Published by Elsevier Masson SAS. All rights reserved.
FAU - Huyut, M T
AU  - Huyut MT
AD  - Department of Biostatistics and Medical Informatics, Medical Faculty, Erzincan 
      Binali Yildirim University, 24100, Erzincan, Turkey.
LA  - eng
PT  - Journal Article
DEP - 20220601
PL  - Netherlands
TA  - Ing Rech Biomed
JT  - Ingenierie et recherche biomedicale : IRBM = Biomedical engineering and research
JID - 101475162
PMC - PMC9158375
OTO - NOTNLM
OT  - Biochemical and hematological biomarkers
OT  - COVID-19
OT  - Classification
OT  - Feature selection methods
OT  - Routine blood values
OT  - Supervised machine learning models
COIS- The authors declare that they have no known competing financial or personal 
      relationships that could be viewed as influencing the work reported in this 
      paper.
EDAT- 2022/06/09 06:00
MHDA- 2022/06/09 06:01
PMCR- 2022/06/01
CRDT- 2022/06/08 01:59
PHST- 2021/10/22 00:00 [received]
PHST- 2022/04/24 00:00 [revised]
PHST- 2022/05/29 00:00 [accepted]
PHST- 2022/06/09 06:00 [pubmed]
PHST- 2022/06/09 06:01 [medline]
PHST- 2022/06/08 01:59 [entrez]
PHST- 2022/06/01 00:00 [pmc-release]
AID - S1959-0318(22)00059-8 [pii]
AID - 100725 [pii]
AID - 10.1016/j.irbm.2022.05.006 [doi]
PST - ppublish
SO  - Ing Rech Biomed. 2023 Feb;44(1):100725. doi: 10.1016/j.irbm.2022.05.006. Epub 
      2022 Jun 1.