Discrimination of SARS-Cov 2 and arboviruses (DENV, ZIKV and CHIKV) clinical features using machine learning techniques: a fast and inexpensive clinical screening for countries simultaneously affected by both diseases
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Abstract
SARS-Cov-2 (Covid-19) has spread rapidly throughout the world, and especially in tropical countries already affected by outbreaks of arboviruses, such as Dengue, Zika and Chikungunya, and may lead these locations to a collapse of health systems. Thus, the present work aims to develop a methodology using a machine learning algorithm (Support Vector Machine) for the prediction and discrimination of patients affected by Covid-19 and arboviruses (DENV, ZIKV and CHIKV). Clinical data from 204 patients with both Covid-19 and arboviruses obtained from 23 scientific articles and 1 dataset were used. The developed model was able to predict 93.1% of Covid-19 cases and 82.1% of arbovirus cases, with an accuracy of 89.1% and Area under Roc Curve of 95.6%, proving to be effective in prediction and possible screening of these patients, especially those affected by Covid-19, allowing early isolation.
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SciScore for 10.1101/2021.01.28.21250714: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources An electronic search was performed in the databases of Periódicos CAPES (portal of the Brazilian development agency CAPES, which aggregates numerous publishers of scientific journals), Google Scholar, Google Dataset and Science Direct, using the keywords, “Clinical features” OR “White blood cells count “OR” Haemogram “AND” coronavirus 2019 “OR” COVID-19 “OR” 2019-nCoV “OR” SARS-CoV-2 “. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)The type of algorithm used was SVM … SciScore for 10.1101/2021.01.28.21250714: (What is this?)
Please note, not all rigor criteria are appropriate for all manuscripts.
Table 1: Rigor
Institutional Review Board Statement not detected. Randomization not detected. Blinding not detected. Power Analysis not detected. Sex as a biological variable not detected. Table 2: Resources
Software and Algorithms Sentences Resources An electronic search was performed in the databases of Periódicos CAPES (portal of the Brazilian development agency CAPES, which aggregates numerous publishers of scientific journals), Google Scholar, Google Dataset and Science Direct, using the keywords, “Clinical features” OR “White blood cells count “OR” Haemogram “AND” coronavirus 2019 “OR” COVID-19 “OR” 2019-nCoV “OR” SARS-CoV-2 “. Google Scholarsuggested: (Google Scholar, RRID:SCR_008878)The type of algorithm used was SVM with Cost regression (c) = 1.50, Kernel type RBF (g = 1,04) and the following optimization parameters: numerical tolerance = 0.005, without iteration limit. Costsuggested: (COST, RRID:SCR_014098)Results from OddPub: We did not detect open data. We also did not detect open code. Researchers are encouraged to share open data when possible (see Nature blog).
Results from LimitationRecognizer: An explicit section about the limitations of the techniques employed in this study was not found. We encourage authors to address study limitations.Results from TrialIdentifier: No clinical trial numbers were referenced.
Results from Barzooka: We did not find any issues relating to the usage of bar graphs.
Results from JetFighter: We did not find any issues relating to colormaps.
Results from rtransparent:- Thank you for including a conflict of interest statement. Authors are encouraged to include this statement when submitting to a journal.
- Thank you for including a funding statement. Authors are encouraged to include this statement when submitting to a journal.
- No protocol registration statement was detected.
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