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

Basit Öğe Kaydı
dc.authorid0000-0003-0924-3685
dc.authorid0000-0001-7767-0342
dc.contributor.authorAtalan, Abdulkadir
dc.contributor.authorSahin, Hasan
dc.contributor.authorAtalan, Yasemin Ayaz
dc.date.accessioned2026-02-12T21:05:19Z
dc.date.available2026-02-12T21:05:19Z
dc.date.issued2022
dc.departmentBursa Teknik Üniversitesi
dc.description.abstractA healthcare resource allocation generally plays a vital role in the number of patients treated (p(nt)) and the patient waiting time (w(t)) in healthcare institutions. This study aimed to estimate p(nt) and w(t) 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 (delta(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 delta(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 delta(0.0), delta(0.1), delta(0.2), and delta(0.3), the accuracy data were calculated as 0.9838, 0.9843, 0.9838, and 0.9846 for p(nt); 0.9514, 0.9517, 0.9514, and 0.9514 for w(t), respectively in the training stage. The GB algorithm had the best performance value, except for the results of the delta(0.2) (AB had a better accuracy at 0.8709 based on the value of delta(0.2) for p(nt)) in the test stage. According to the AB algorithm based on the delta(0.0), delta(0.1), delta(0.2), and delta(0.3), the accuracy data were calculated as 0.7956, 0.9298, 0.8288, and 0.7394 for p(nt); 0.8820, 0.8821, 0.8819, and 0.8818 for w(t) in the training phase, respectively. All scenarios created by the delta(i) coefficient should be preferred for ED since the income provided by the p(nt) value to the hospital was more than the cost of healthcare resources. On the contrary, the w(t) estimation results of ML algorithms based on the delta(i) coefficient differed. Although w(t) values in all ML algorithms with delta(0.0) and delta(0.1) coefficients reduced the cost of the hospital, w(t) values based on delta(0.2) and delta(0.3) increased the cost of the hospital.
dc.identifier.doi10.3390/healthcare10101920
dc.identifier.issn2227-9032
dc.identifier.issue10
dc.identifier.pmid36292372
dc.identifier.scopus2-s2.0-85140611752
dc.identifier.scopusqualityQ1
dc.identifier.urihttps://doi.org/10.3390/healthcare10101920
dc.identifier.urihttps://hdl.handle.net/20.500.12885/6904
dc.identifier.volume10
dc.identifier.wosWOS:000875871200001
dc.identifier.wosqualityQ2
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.indekslendigikaynakPubMed
dc.language.isoen
dc.publisherMdpi
dc.relation.ispartofHealthcare
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WoS_20260212
dc.subjecthealthcare resources
dc.subjectp(nt) and w(t)
dc.subjectdiscrete-event simulation
dc.subjectmachine learning
dc.subjectcost analysis
dc.titleIntegration of Machine Learning Algorithms and Discrete-Event Simulation for the Cost of Healthcare Resources
dc.typeArticle

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