International Journal of Scientific Research and Engineering Development

In this project, I developed a simulation to model how waiting lines form inside a hospital and used machine learning to predict whether a patient would experience a short or long waiting time. The synthetic dataset included realistic variables such as arrival time, number of patients in queue, number of staff, service duration, and day type. Using a Logistic Regression classifier, the model achieved strong predictive performance with high accuracy, precision, recall, and F1-score. These findings demonstrate how combining simulation with machine learning can provide decision support for improving patient flow and hospital resource management.