Banking Queue Waiting Time Prediction based on Predicted Service Time using Support Vector Regression

Digital technologies have been contributing to providing quality health care to patients. One aspect of this is providing accurate wait times for patients waiting to be serviced at healthcare facilities. This is naturally a complex problem as there is a multitude of factors that can impact the wait time. However, the problem becomes even more complex if the patient's journey requires visiting multiple stations in the hospital; such as having vital signs taken, doing an ultrasound, and seeing a specialist. The authors aim to provide an accurate method for estimating the wait time by utilising a real dataset of transactions collected from a major hospital over a year. The work employs feature engineering and compares several machine learning‐based algorithms to predict patients' waiting times for single‐stage and multi‐stage services. The Random Forest algorithm achieved the lowest root mean squared error (RMSE) value of 6.69 min among all machine learning algorithms. The results were also compared against a formula‐based system used in the industry, and the proposed model outperformed the existing model, showing improvements of 25.1% in RMSE and 18.9% in MAE metrics. These findings indicate a significant improvement in the accuracy of predicting waiting times compared to existing techniques.

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Banking Queue Waiting Time Prediction based on Predicted Service Time using Support Vector Regression

Cited by 3 publications

References 12 publications

Mixture Density Networks as a General Framework for Estimation and Prediction of Waiting Time Distributions in Queueing Systems

Mixture Density Networks as a General Framework for Estimation and Prediction of Waiting Time Distributions in Queueing Systems

TGNN: A GNN-Based Method with Multi-entity Node for Personal Banking Time Prediction

A machine learning‐based approach for wait‐time estimation in healthcare facilities with multi‐stage queues

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