Machine Learning Model Development based on Brazil’s Covid-19 Data Set

Abstract

Brazil is one of the countries most affected by the COVID-19 pandemic, with more than 16 million confirmedcases and 454,429 confirmed deaths by May 26, 2021 (according to the Johns Hopkins Coronavirus Resource Center). Brazil was and still is one of the countries most impacted by the first wave of Covid-19 which first recorded case on 26^th February 2020 and reached community transmission from 20^th March 2019 onwards to dates, that caught Brazil unprepared and unable to response due to the strain on hospital capacity such as the intense and lengthy request for ICU (incentive care unit) beds, professionals, personal protection equipment and healthcare resources. A data science team at Sirio Libanês, a top-tier hospital in Brazil, decided to use ML to help reduce the strain on hospital’s ICU beds where the objective is to develop a ML model to predict if a patient of confirmed COVID-19 case would require admission to the ICU.  With that objective in mind, the team has collected a decent amount of clinical data from patients i.e. the features of COVID patients, and the target (those been admitted to ICU). The paper describes the complete Model design using features of contemporary Machine Learning Models to interpret the Collected Data from the hospital to produce a decent report as a solution to the above mentioned hindrances at crucial times.

The dataset is released on Kaggle platform with full data description at the following URL (https://www.kaggle.com/S%C3%ADrio-Libanes/covid19) by the team seeking interesting solutions and findings from the public