Abstract:
Covid-19 is a highly contagious infectious virus that became a pandemic and had a major
impact on global public health, resulting in deaths and serious health repercussions.
It affects the human body in a variety of ways, including respiratory problems and
multi-organ failure, all of which can result in death in a short period of time. In the
absence of comprehensive medical treatment and with the risk of new viral variants
emerging, the global mortality rate rises every day regardless of the fact that strict
social separation, lockdowns, and safety precautions are in place. Excessive attention
on early treatment options could help reduce mortality risk of individuals affected by
the virus. Laboratory tests, medical check-up reports, and clinical biomarkers can be
used to determine an individual’s health status. In this backdrop, various researchers
have proposed machine learning algorithms to reliably forecast the severity of Covid-
19 disease. Multiple machine learning algorithms are used in this study to compare
and choose the best model for predicting how long a patient will survive a coronavirus
infection. Furthermore, the author determined which variables had the highest impact
on the model’s accuracy. Two machine learning algorithms namely Decision Tree and
Random Forest were applied to predict the mortality rate. Data from 4229 individuals
infected with Covid-19 were used in the study. The potential for effective death prediction
was evaluated using 16 variables based on clinical laboratory data of Covid-19
infected patients. The data was standardized and processed using various pre-processing
techniques before being fed into the models. From among the two models, Decision Tree
yielded a higher accuracy of 95.75%, an average precision, recall, and F-measure of
0.958%, and a lower mean absolute error rate of 0.051. The findings suggest that using
the Decision Tree algorithm to estimate the mortality of Covid-19 patients can lead to
a more accurate final prediction model.
