Risk Prediction of COVID-19 Positive Patients: How well does the machine learning tools perform?

Abstract

The pandemic of COVID-19 is spreading everywhere in the world which subsequently has led the world into the most existential health emergency, even in the second wave. Machine learning (ML) has already proved as a promising field to guide the future course of actions in healthcare as a part of combat the pandemic. In this paper, we have applied five algorithms, namely, Random Forest, Decision Tree, Ctree, Naïve Bayes, and PCA have been used to forecast the threatening death risk among the confirmed cases of Covid-19 patients. Since COVID-19 disease is more prevalent in the lungs so we’ve divided our data into two parts and applied the ML methods on it. Three different predictions have been showed by five of the ML models, where the decision tree for outcome-1, outcome-2 outperforms, and the random forest for outcome-3 performs best than the rest of all. In particular, the results show that which method works best on COVID-19 dataset as well as the prior indication of adverse health factors of the infected patient. Finally, we showed them the alive and death prediction percentage for randomly selected ten patients that demonstrate the capability of ML models. By these sorts of research, we can Figure out whether the affected people have to be taken to ICU or ought to be dealt with at home. Moreover, accuracy performance metric has been determined in two different testing set to identify the most efficient model for risk prediction.