  This work proposes a framework for optimizing machine learning algorithms.
The practicality of the framework is illustrated using an important case study
from the healthcare domain, which is predicting the admission status of
emergency department (ED) patients (e.g., admitted vs. discharged) using
patient data at the time of triage. The proposed framework can mitigate the
crowding problem by proactively planning the patient boarding process. A large
retrospective dataset of patient records is obtained from the electronic health
record database of all ED visits over three years from three major locations of
a healthcare provider in the Midwest of the US. Three machine learning
algorithms are proposed: T-XGB, T-ADAB, and T-MLP. T-XGB integrates extreme
gradient boosting (XGB) and Tabu Search (TS), T-ADAB integrates Adaboost and
TS, and T-MLP integrates multi-layer perceptron (MLP) and TS. The proposed
algorithms are compared with the traditional algorithms: XGB, ADAB, and MLP, in
which their parameters are tunned using grid search. The three proposed
algorithms and the original ones are trained and tested using nine data groups
that are obtained from different feature selection methods. In other words, 54
models are developed. Performance was evaluated using five measures: Area under
the curve (AUC), sensitivity, specificity, F1, and accuracy. The results show
that the newly proposed algorithms resulted in high AUC and outperformed the
traditional algorithms. The T-ADAB performs the best among the newly developed
algorithms. The AUC, sensitivity, specificity, F1, and accuracy of the best
model are 95.4%, 99.3%, 91.4%, 95.2%, 97.2%, respectively.
