Application of predictive modeling via decision tree in cases of Severe Acute Respiratory Syndrome (SARS), with emphasis on Corona Virus Disease 2019 (COVID-19) in Brazil for the period from 2020 to 2022
DOI:
https://doi.org/10.33448/rsd-v11i15.36173Keywords:
Decision tree; COVID-19; Statistic; Prediction; SARS.Abstract
Severe Acute Respiratory Syndrome (SARS) covers cases of Influenza Syndrome (GS) that evolve with compromised respiratory function which, in most cases, leads to hospitalization. The pandemic caused by the Corona Virus Disease (COVID-19) has become the new global challenge. Patients who had certain chronic diseases had a worse prognosis when they were introduced to the new coronavirus. It is essential to determine the main risk groups for any disease, since it facilitates the decision-making of health professionals. This research aimed to apply predictive modeling via decision tree to estimate the probability of the individual who: has SARS being cured or dying and has SARS being cured or dying due to contamination and not contamination by COVID -19, finally analyzing the results (cases registered in Brazil). This information will help healthcare professionals understand how each comorbidity behaved. The main results showed that the proposed model fits well, finding the following survival percentages: it is better for the individual who presented symptoms of SARS to have kidney disease and asthma than to have no comorbidity, as the chance of cure is 7% higher; it is better for the individual who presented symptoms of SARS due to contamination by COVID-19 to have neurological, cardiovascular and hematological disease than to have no comorbidity, as the chance of cure is 14% higher and, finally, the individual who presented symptoms of SARS , but who has not been infected by COVID-19 has a 75% chance of cure.
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Copyright (c) 2022 Miriam Lecília Farias Ribeiro; Natália Moraes Cordeiro; Dâmocles Aurélio Nascimento da Silva Alves
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