Integration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources

A healthcare resource allocation generally plays a vital role in the number of patients treated (p) and the patient waiting time (w) in healthcare institutions. This study aimed to estimate p and w as output variables by considering the number of healthcare resources employed and analyze the cost of health resources to the hospital depending on the cost coefficient (δ_i) in an emergency department (ED). The integration of the discrete-event simulation (DES) model and machine learning (ML) algorithms, namely random forest (RF), gradient boosting (GB), and AdaBoost (AB), was used to calculate the estimation of the output variables depending on the δ_i of resources cost. The AB algorithm performed best in almost all scenarios based on the results of the analysis. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p; 0.9514, 0.9517, 0.9514, and 0.9514 for w, respectively in the training stage. The GB algorithm had the best performance value, except for the results of the δ_0.2 (AB had a better accuracy at 0.8709 based on the value of δ_0.2 for p) in the test stage. According to the AB algorithm based on the δ_0.0, δ_0.1, δ_0.2, and δ_0.3, the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p; 0.8820, 0.8821, 0.8819, and 0.8818 for w in the training phase, respectively. All scenarios created by the δ_i coefficient should be preferred for ED since the income provided by the p value to the hospital was more than the cost of healthcare resources. On the contrary, the w estimation results of ML algorithms based on the δ_i coefficient differed. Although w values in all ML algorithms with δ_0.0 and δ_0.1 coefficients reduced the cost of the hospital, w values based on δ_0.2 and δ_0.3 increased the cost of the hospital.