  A prompt severity assessment model of patients confirmed for having
infectious diseases could enable efficient diagnosis while alleviating burden
on the medical system. This study aims to develop a SARS-CoV-2 severity
assessment model and establish a medical system that allows patients to check
the severity of their cases and informs them to visit the appropriate clinic
center based on past treatment data of other patients with similar severity
levels. This paper provides the development processes of a severity assessment
model using machine learning techniques and its application on SARS-CoV-2
patients. The proposed model is trained on a nationwide dataset provided by a
Korean government agency and only requires patients' basic personal data,
allowing them to judge the severity of their own cases. After modeling, the
boosting-based decision tree model was selected as the classifier while
mortality rate was interpreted as the probability score. The dataset was
collected from all Korean citizens who were confirmed with COVID-19 between
February, 2020 and July, 2021. The experiments achieved high model performance
with an approximate precision of $0{\cdot}923$ and AUROC score of $0{\cdot}950$
[$95$% Tolerance Interval $0{\cdot}940$-$0{\cdot}958$, $95$% Confidence
Interval $0{\cdot}949$-$0{\cdot}950$]. Moreover, our experiments identified the
most important variables affecting the severity in the model via sensitivity
analysis. The prompt severity assessment model for managing infectious people
has been attained through using a nationwide dataset. It has demonstrated its
superior performance by surpassing that of conventional risk assessments. With
the model's high performance and easily accessible features, the triage
algorithm is expected to be particularly useful when patients monitor their
health status by themselves through smartphone applications.
