Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach

Healthcare systems face significant challenges and financial burdens due to patient no-shows, highlighting the need for accurate and interpretable predictive models. This study evaluated the efficacy of twelve classification algorithms that can generate self-explanatory results illustrations across...

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Bibliographic Details
Published in:	IEEE access Vol. 12; pp. 166058 - 166067
Main Authors:	Toffaha, Khaled M., Simsekler, Mecit Can Emre, Alshehhi, Aamna, Atif Omar, Mohammed
Format:	Journal Article
Language:	English
Published:	IEEE 2024
Subjects:	Accuracy additive model trees Classification algorithms decision tree algorithms greedy tree classifiers healthcare analytics hierarchical shrinkage trees Hospitals Machine learning Measurement Medical services Patient monitoring Patient no-shows Prediction algorithms Predictive models Random forests Socioeconomics Training
ISSN:	2169-3536, 2169-3536
Online Access:	Get full text
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