Decision analysis framework for predicting no-shows to appointments using machine learning algorithms.

ABSTRACT:

Background

No-show to medical appointments has significant adverse effects on healthcare systems and their clients. Using machine learning to predict no-shows allows managers to implement strategies such as overbooking and reminders targeting patients most likely to miss appointments, optimizing the use of resources.

Methods

In this study, we proposed a detailed analytical framework for predicting no-shows while addressing imbalanced datasets. The framework includes a novel use of z-fold cross-validation performed twice during the modeling process to improve model robustness and generalization. We also introduce Symbolic Regression (SR) as a classification algorithm and Instance Hardness Threshold (IHT) as a resampling technique and compared their performance with that of ot

SUBMITTER: Deina C 

PROVIDER: S-EPMC10770919 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature