  COVID-19 continues to be considered an endemic disease in spite of the World
Health Organization's declaration that the pandemic is over. This pandemic has
disrupted people's lives in unprecedented ways and caused widespread morbidity
and mortality. As a result, it is important for emergency physicians to
identify patients with a higher mortality risk in order to prioritize hospital
equipment, especially in areas with limited medical services. The collected
data from patients is beneficial to predict the outcome of COVID-19 cases,
although there is a question about which data makes the most accurate
predictions. Therefore, this study aims to accomplish two main objectives.
First, we want to examine whether deep learning algorithms can predict a
patient's morality. Second, we investigated the impact of Clinical and RT-PCR
on prediction to determine which one is more reliable. We defined four stages
with different feature sets and used interpretable deep learning methods to
build appropriate model. Based on results, the deep neural decision forest
performed the best across all stages and proved its capability to predict the
recovery and death of patients. Additionally, results indicate that Clinical
alone (without the use of RT-PCR) is the most effective method of diagnosis,
with an accuracy of 80%. It is important to document and understand experiences
from the COVID-19 pandemic in order to aid future medical efforts. This study
can provide guidance for medical professionals in the event of a crisis or
outbreak similar to COVID-19.
