• Layla Hamad
• Layan Mohammed
• Rinad Misfer
• Ahmad M. Alkheder Almasabi

This paper presents a combined simulation and machine learning approach to predicting clinic crowding. A discrete-event simulation was developed using SimPy to model patient arrivals, waiting times, and doctor availability. The simulation-generated dataset was used to train a Logistic Regression classifier, which achieved perfect accuracy. The results demonstrate the effectiveness of integrating simulation with machine learning for analyzing clinic performance.

Clinic Crowding, Simulation, SimPy, Machine Learning, Logistic Regression, Healthcare Modeling

"Clinic Crowding Prediction Using SimPy Simulation and Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org), ISSN:2349-5162, Vol.12, Issue 12, page no.b1-b2, December-2025, Available :http://www.jetir.org/papers/JETIR2512101.pdf

"Clinic Crowding Prediction Using SimPy Simulation and Logistic Regression", International Journal of Emerging Technologies and Innovative Research (www.jetir.org | UGC and issn Approved), ISSN:2349-5162, Vol.12, Issue 12, page no. ppb1-b2, December-2025, Available at : http://www.jetir.org/papers/JETIR2512101.pdf