• Randa Abdulhadi Al Mansour
• Ghala Mohammed Al Mansour
• Dr. Ahmad Mohammed Al Mosabi

This project presents a combined machine learning and simulation approach to predicting and analyzing patient waiting times in the Emergency Department (ED). A Random Forest classifier was trained using a synthetic dataset of 500 patient encounters to classify waiting time into short-wait and long-wait categories. The model achieved high performance, with an accuracy of 95.33% and an F1-score of 96.26%. In the second phase, a discrete-event simulation was implemented using the SimPy environment to model patient arrivals, queuing behavior, and doctor resource utilization in the ED. The simulation produced operational indicators such as average waiting time and total number of patients served. Together, the machine learning model and the simulation provide a comprehensive understanding of ED performance and support decisions related to patient flow and resource management.

Emergency Department, Waiting Time Prediction, Machine Learning, Random Forest, Simulation, SimPy, Healthcare Analytics

"EMERGENCY DEPARTMENT WAITING TIME ANALYSIS USING MACHINE LEARNING AND SIMULATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.b272-b274, December-2025, Available :http://www.jetir.org/papers/JETIR2512141.pdf

"EMERGENCY DEPARTMENT WAITING TIME ANALYSIS USING MACHINE LEARNING AND SIMULATION", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppb272-b274, December-2025, Available at : http://www.jetir.org/papers/JETIR2512141.pdf