Authors : Pyelshak Yusuf; Fatima Umar Zambuk; Badamasi Imam Yau; Solomon Rifkatu Aaron; Atangs Ishaku; Aminu Agabus; Solomon Panshak Dawal; Ismail Zahraddeen Yakubu

Volume/Issue : Volume 9 - 2024, Issue 3 - March

Google Scholar : https://tinyurl.com/2vye9ks5

Scribd : https://tinyurl.com/5ykxtexn

DOI : https://doi.org/10.38124/ijisrt/IJISRT24MAR1035

Note : A published paper may take 4-5 working days from the publication date to appear in PlumX Metrics, Semantic Scholar, and ResearchGate.

Abstract : The proposed thesis aims to optimize patient flow and reduce waiting times in emergency departments using simulation modeling and the Random Forest algorithm. Emergency departments face significant challenges in managing patient flow and reducing waiting times, which can lead to increased patient dissatisfaction and decreased quality of care. The proposed solution uses simulation modeling to create a virtual model of the emergency department and simulate patient flow under different scenarios. The Random Forest algorithm is then used to analyze the simulation results and identify the factors impacting patient flow and waiting times. By optimizing these factors, the proposed solution aims to reduce waiting times and improve the overall patient experience. The research involves the development and validation of the simulation model and the implementation of the Random Forest algorithm using real-world emergency department data. The outcomes of the implemented Random Forest Model in Chapter Four showcase its efficacy with an accuracy rate of 0.85, sensitivity rate of 0.99, and other favorable metrics. The proposed solution has the potential to improve patient outcomes and reduce costs associated with emergency department overcrowding and delays.

Keywords : Emergency Department, Patient Flow Control, Machine Learning Algorithm, Simulation Model.

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