PMID- 37649715
OWN - NLM
STAT- MEDLINE
DCOM- 20230901
LR  - 20230901
IS  - 1537-744X (Electronic)
IS  - 2356-6140 (Print)
IS  - 1537-744X (Linking)
VI  - 2023
DP  - 2023
TI  - SARS-CoV-2 Prediction Strategy Based on Classification Algorithms from a Full 
      Blood Examination.
PG  - 3248192
LID - 10.1155/2023/3248192 [doi]
LID - 3248192
AB  - A fast and efficient diagnosis of serious infectious diseases, such as the recent 
      SARS-CoV-2, is necessary in order to curb both the spread of existing variants 
      and the emergence of new ones. In this regard and recognizing the shortcomings of 
      the reverse transcription-polymerase chain reaction (RT-PCR) and rapid diagnostic 
      test (RDT), strategic planning in the public health system is required. In 
      particular, helping researchers develop a more accurate diagnosis means to 
      distinguish patients with symptoms with COVID-19 from other common infections is 
      what is needed. The aim of this study was to train and optimize the support 
      vector machine (SVM) and K-nearest neighbors (KNN) classifiers to rapidly 
      identify SARS-CoV-2 (positive/negative) patients through a simple complete blood 
      test without any prior knowledge of the patient's health state or symptoms. After 
      applying both models to a sample of patients at Israelita Albert Einstein at Sao 
      Paulo, Brazil (solely for two examined groups of patients' data: "regular ward" 
      and "not admitted to the hospital"), it was found that both provided early and 
      accurate detection, based only on a selected blood profile via the statistical 
      test of dependence (ANOVA test). The best performance was achieved by the 
      improved SVM technique on nonhospitalized patients, with precision, recall, 
      accuracy, and AUC values reaching 94%, 96%, 95%, and 99%, respectively, which 
      supports the potential of this innovative strategy to significantly improve 
      initial screening.
CI  - Copyright (c) 2023 C. F. Choukhan et al.
FAU - Choukhan, C F
AU  - Choukhan CF
AUID- ORCID: 0000-0002-9927-2887
AD  - Laboratory of Mathematics, Computing and Applications, Mohammed V University in 
      Rabat, Faculty of Sciences, Rabat, Morocco.
FAU - Lasri, I
AU  - Lasri I
AUID- ORCID: 0000-0002-1481-094X
AD  - Laboratory of Conception and Systems (Electronics, Signals and Informatics), 
      Mohammed V University in Rabat, Faculty of Sciences, Rabat, Morocco.
FAU - El Hatimi, R
AU  - El Hatimi R
AUID- ORCID: 0000-0002-6532-8045
AD  - Laboratory of Mathematics, Computing and Applications, Mohammed V University in 
      Rabat, Faculty of Sciences, Rabat, Morocco.
FAU - Lemnaouar, M R
AU  - Lemnaouar MR
AUID- ORCID: 0000-0002-1077-331X
AD  - LASTIMI, Mohammed V University in Rabat, Superior School of Technology, Sale, 
      Rabat, Morocco.
FAU - Esghir, M
AU  - Esghir M
AUID- ORCID: 0000-0002-5365-8784
AD  - Laboratory of Mathematics, Computing and Applications, Mohammed V University in 
      Rabat, Faculty of Sciences, Rabat, Morocco.
LA  - eng
PT  - Journal Article
DEP - 20230822
PL  - United States
TA  - ScientificWorldJournal
JT  - TheScientificWorldJournal
JID - 101131163
SB  - IM
MH  - Humans
MH  - *SARS-CoV-2/genetics
MH  - *COVID-19/diagnosis
MH  - Brazil
MH  - Algorithms
MH  - Cluster Analysis
PMC - PMC10465262
COIS- The authors declare that they have no conflicts of interest.
EDAT- 2023/08/31 06:42
MHDA- 2023/09/01 06:42
PMCR- 2023/08/22
CRDT- 2023/08/31 04:03
PHST- 2022/10/22 00:00 [received]
PHST- 2023/07/01 00:00 [revised]
PHST- 2023/08/03 00:00 [accepted]
PHST- 2023/09/01 06:42 [medline]
PHST- 2023/08/31 06:42 [pubmed]
PHST- 2023/08/31 04:03 [entrez]
PHST- 2023/08/22 00:00 [pmc-release]
AID - 10.1155/2023/3248192 [doi]
PST - epublish
SO  - ScientificWorldJournal. 2023 Aug 22;2023:3248192. doi: 10.1155/2023/3248192. 
      eCollection 2023.