Predicting Hospital No-Shows: Interpretable Machine Learning Models Approach

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
Main Authors: Khaled M. Toffaha, Mecit Can Emre Simsekler, Aamna Alshehhi, Mohammed Atif Omar
Format: Article
Language:English
Published: IEEE 2024-01-01
Series:IEEE Access
Subjects:
Patient no-shows
decision tree algorithms
greedy tree classifiers
additive model trees
hierarchical shrinkage trees
healthcare analytics
Online Access:https://ieeexplore.ieee.org/document/10741524/
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https://ieeexplore.ieee.org/document/10741524/

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