A Machine Learning Approach to Predicting Covid-19 Cases Amongst Suspected Cases and Their Category of Admission

Abstract

The rapid spread of COVID-19 worldwide has claimed thousands of lives and has put unprecedented pressure on the healthcare systems around the world. The World Health Organization has emphasised the need for comprehensive testing in order to fight the virus [1]. With the lack of testing kits available worldwide, there is a call for novel testing methods that can help arrest the spread faster [18]. Every health care worker exposed to the virus puts additional pressure on an already outstretched infrastructure. Here, in this study we propose a machine learning approach towards predicting Covid-19 cases among a sample population who have undergone other clinical tests and blood spectrum tests. The patient data used in this effort has been donated by Hospital Israelita Albert Einstein, at São Paulo, Brazil [6] for the purpose of research. The problem at hand is divided into two parts: Predict confirmed COVID-19 cases amongst suspected cases based on the laboratory tests of their clinical samples. Predict admission to general, semi-ICU, and ICU wards among those who predicted positive for COVID-19 in the first task. Our approach uses Classification from Supervised Learning techniques to solve this problem. The efficacy of this approach could be used to scale and develop automated systems that could predict the likeliness of Covid-19 based on laboratory tests that are readily accessible. From the features presented to us in the dataset, we are able to predict with 87.0-97.4 percent accuracy at a 95 percent confidence level that a patient is suffering from Covid-19 when biomarkers are taken into consideration. Among those that tested positive, we were able to demonstrate that our model could predict with 87.0-100 percent accuracy at a 95 percent confidence that whether the patient would be admitted to a particular ward.