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
| Autor: | Castro Jds |
|---|---|
| Rok vydání: | 2021 |
| Předmět: |
Coronavirus disease 2019 (COVID-19)
Clinical screening business.industry Severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Outbreak medicine.disease medicine.disease_cause Machine learning computer.software_genre Arbovirus Dengue fever medicine Chikungunya Artificial intelligence business computer Healthcare system |
| DOI: | 10.1101/2021.01.28.21250714 |
| Popis: | 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. |
| Databáze: | OpenAIRE |
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