Comparative Analysis of Machine Learning Algorithms for Predicting Consultation Wait Times in Outpatient Clinics
Comparative Analysis of Machine Learning Algorithms for Predicting Consultation Wait Times in Outpatient Clinics |
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| © 2025 by IJETT Journal | ||
| Volume-73 Issue-2 |
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| Year of Publication : 2025 | ||
| Author : Jeffin Joseph, S. Senith, A. Alfred Kirubaraj, S.R Jino Ramson |
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| DOI : 10.14445/22315381/IJETT-V73I2P108 | ||
How to Cite?
Jeffin Joseph, S. Senith, A. Alfred Kirubaraj, S.R Jino Ramson, "Comparative Analysis of Machine Learning Algorithms for Predicting Consultation Wait Times in Outpatient Clinics," International Journal of Engineering Trends and Technology, vol. 73, no. 2, pp. 92-106, 2025. Crossref, https://doi.org/10.14445/22315381/IJETT-V73I2P108
Abstract
Accurate prediction of consultation wait times significantly improves patient satisfaction and operational efficiency in outpatient clinics. This paper develops predictive models for consultation wait times using machine learning techniques, which include comprehensive predictors: patient demographics, temporal factors, queueing metrics, and historical wait time data. Fifteen Machine Learning models, including six regression and nine classification algorithms, were trained and evaluated using a dataset of 28,787 patient records from a multispecialty hospital. Results show that the tree-based models have better performance, in which the Decision Tree Regressor has the best performance among regression models with R² = 0.98, MAE = 0.40, and RMSE = 3.75, while the Random Forest Classifier is among the classification models with Accuracy = 95.65%, ROC-AUC = 98.91%. The analysis of feature importance using linear regression underscores the dominance of temporal factors and queueing metrics over demographic and historical wait time predictors in determining consultation wait times. This paper demonstrates that machine learning algorithms could be a good approach to predicting consultation wait time to help clinic operations. However, these findings need to be interpreted in the context of limitations in the dataset and the exclusion of some potentially important predictors. Future studies should validate the models in different clinical settings and include more variables to increase their generalizability and clinical usefulness.
Keywords
Hospital management, Machine learning, Wait time, Outpatient clinic, Random forest, Decision tree.
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