Predicting no-show medical appointments using machine learning
Alshaya, Sara, McCarren, AndrewORCID: 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
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
Advances in Data Science, Cyber Security and IT Applications. Communications in Computer and Information Science
1097(1).
Springer. ISBN 978-3-030-36364-2