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

An Integrated Optimization and Machine Learning Models to Predict the Admission Status of Emergency Patients