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Predicting no-show medical appointments using machine learning

Alshaya, Sara, McCarren, Andrew orcid logoORCID: 0000-0002-7297-0984 and Al-Rasheed, Amal (2019) Predicting no-show medical appointments using machine learning. In: International Conference on Computing (ICC2019), 10-12 Dec 2019, Riyadh, Saudi Arabia. ISBN 978-3-030-36364-2

Abstract
Health care centers face many issues due to the limited availability of resources, such as funds, equipment, beds, physicians, and nurses. Appointment absences lead to a waste of hospital resources as well as endangering patient health. This fact makes unattended medi- cal appointments both socially expensive and economically costly. This research aimed to build a predictive model to identify whether an appointment would be a no-show or not in order to reduce its consequences. This paper proposes a multi-stage framework to build an accurate predictor that also tackles the imbalanced property that the data exhibits. The first stage includes dimensionality reduction to compress the data into its most important components. The second stage deals with the imbalanced nature of the data. Different machine learning algorithms were used to build the classifiers in the third stage. Various evaluation metrics are also discussed and an evaluation scheme that fits the problem at hand is described. The work presented in this paper will help decision makers at health care centers to implement effective strategies to reduce the number of no-shows.
Metadata
Item Type:Conference or Workshop Item (Paper)
Event Type:Conference
Refereed:Yes
Uncontrolled Keywords:Deep learning; No-show; Data imbalance; Dimensionality reduction
Subjects:Computer Science > Machine learning
DCU Faculties and Centres:DCU Faculties and Schools > Faculty of Engineering and Computing > School of Computing
Research Initiatives and Centres > INSIGHT Centre for Data Analytics
Published in: Advances in Data Science, Cyber Security and IT Applications. Communications in Computer and Information Science 1097(1). Springer. ISBN 978-3-030-36364-2
Publisher:Springer
Official URL:http://dx.doi.org/10.1007/978-3-030-36365-9_18
Copyright Information:© 2019 Springer
Use License:This item is licensed under a Creative Commons Attribution-NonCommercial-Share Alike 3.0 License. View License
ID Code:25441
Deposited On:29 Jan 2021 17:41 by Michael Scriney . Last Modified 29 Jan 2021 17:41
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