COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients

The Coronavirus Disease 2019 (COVID-19) pandemic has had social and clinical effects over the healthcare systems. Globally, over 178.602.416 confirmed cases and 3.868.228 deaths have been recorded as of 21st June 2021, based on the Dashboard updated by the Center for Systems Science and Engineering (CSSE) at Johns Hopkins University. The continuous rise in the number of asymptomatic, pre-symptomatic and symptomatic patients has developed efficient and accessible models for prediction using Open Source Libraries and Cloud Services. This paper is proposes a different machine learning algorithms in order to analyse the COVID-19 OpenData Resources from Mexico and Brazil, which represent the two major Population, Economics and Affected by Disease countries in Latin America. This model uses only the COVID-19 patient's geographical, social conditions, economics conditions, clinical risk factors, symptoms reports and demographic data to predict the recovery and death. The model of Mexico has an accuracy of 93% and the perceived mean of the recall and the precision (F1 Score) of 0.79 on the dataset used. On the other hand, the model of Brazil has an accuracy of 69% and F1 Score of 0.75 on the dataset studied. The result considers data from patients under the age of 0 and 120 years. The contribution of the work is the application of Big Data technologies and Machine Learning algorithms using Open Resource Libraries and Amazon Cloud Services with the vision to improve the clinical diagnosis, even infectious Disease with mathematical approaches