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

Carolina Deina1, Flavio S Fogliatto2, Giovani J C da Silveira3

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Summary

This study introduces Symbolic Regression (SR) and Instance Hardness Threshold (IHT) for predicting patient no-shows, outperforming existing methods. The novel approach ensures robust model generalization for healthcare resource optimization.

Keywords:
Classification algorithmsHealthcare environmentsImbalanced datasetMachine learningMissed appointmentsResampling techniques

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Area of Science:

  • Healthcare Analytics
  • Machine Learning
  • Data Science

Background:

  • Patient no-shows negatively impact healthcare systems and patient outcomes.
  • Machine learning offers a solution for predicting no-shows, enabling proactive resource management.

Purpose of the Study:

  • To develop and evaluate a novel framework for predicting patient no-shows, specifically addressing imbalanced datasets.
  • To introduce and assess Symbolic Regression (SR) and Instance Hardness Threshold (IHT) for no-show prediction.

Main Methods:

  • A framework incorporating a novel double z-fold cross-validation was proposed.
  • Symbolic Regression (SR) and Instance Hardness Threshold (IHT) were compared against KNN, SVM, RUS, SMOTE, and NearMiss-1.
  • The framework was validated on two Brazilian hospital datasets with varying no-show rates.

Main Results:

  • Symbolic Regression (SR) and Instance Hardness Threshold (IHT) demonstrated superior performance in predicting patient no-shows.
  • Instance Hardness Threshold (IHT) showed excellent results across all tested classification algorithms with low performance variability.
  • The study achieved high sensitivity outcomes, exceeding 0.94 on both datasets, outperforming existing literature.

Conclusions:

  • This research is the first to apply SR and IHT for patient no-show prediction and to implement double z-fold cross-validation.
  • The findings underscore the risks of biased results from insufficient validation runs on imbalanced datasets.
  • The proposed framework enhances model generalization and stability analysis for accurate no-show prediction.