  Patient triage at emergency departments (EDs) is necessary to prioritize care
for patients with critical and time-sensitive conditions. Different tools are
used for patient triage and one of the most common ones is the emergency
severity index (ESI), which has a scale of five levels, where level 1 is the
most urgent and level 5 is the least urgent. This paper proposes a framework
for utilizing machine learning to develop an e-triage tool that can be used at
EDs. A large retrospective dataset of ED patient visits is obtained from the
electronic health record of a healthcare provider in the Midwest of the US for
three years. However, the main challenge of using machine learning algorithms
is that most of them have many parameters and without optimizing these
parameters, developing a high-performance model is not possible. This paper
proposes an approach to optimize the hyperparameters of machine learning. The
metaheuristic optimization algorithms simulated annealing (SA) and adaptive
simulated annealing (ASA) are proposed to optimize the parameters of extreme
gradient boosting (XGB) and categorical boosting (CaB). The newly proposed
algorithms are SA-XGB, ASA-XGB, SA-CaB, ASA-CaB. Grid search (GS), which is a
traditional approach used for machine learning fine-tunning is also used to
fine-tune the parameters of XGB and CaB, which are named GS-XGB and GS-CaB. The
six algorithms are trained and tested using eight data groups obtained from the
feature selection phase. The results show ASA-CaB outperformed all the proposed
algorithms with accuracy, precision, recall, and f1 of 83.3%, 83.2%, 83.3%,
83.2%, respectively.
