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Predicting Treatment Response to Image-Guided Therapies Using Machine Learning: An Example for Trans-Arterial Treatment of Hepatocellular Carcinoma

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Predicting no-shows at outpatient appointments in internal medicine using machine learning models.

Felipe Ocampo Osorio^1,2,3,4, Santiago Pedroza Gomez^1,2, David Esteban Rebellón Sanchez^1,2

¹Unidad de Inteligencia Artificial, Fundación Valle del Lili, Cali, Valle del Cauca, Colombia.

Peerj. Computer Science

|June 26, 2025

Summary

Machine learning models can predict patient no-shows in internal medicine, identifying high-risk individuals. This improves healthcare efficiency and patient care by optimizing appointment scheduling and resource allocation.

Keywords:

Internal medicine Machine learning Medical appointments No-shows Non-attendance

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Area of Science:

Healthcare Informatics
Machine Learning in Medicine
Clinical Operations

Background:

Patient absenteeism in medical appointments causes significant healthcare delivery delays and operational inefficiencies.
Addressing no-shows is critical in internal medicine, which manages complex chronic conditions.

Purpose of the Study:

To develop and evaluate machine learning models for predicting patient absenteeism risk in an internal medicine department.
To identify key factors influencing medical appointment no-shows.

Main Methods:

Statistical analysis of institutional data to identify absenteeism predictors.
Evaluation of seven machine learning models, including data processing and class imbalance techniques (SMOTE).
Hyperparameter optimization and model selection using Bagging RandomForest, with SHAP for interpretability.

Main Results:

The Bagging RandomForest model, optimized and balanced with SMOTE, achieved 84.80% predictive accuracy.
Key predictors included previous absences, appointment timing, and diagnosed diseases.
SHAP analysis provided interpretability, highlighting influential variables.

Conclusions:

Machine learning effectively predicts internal medicine patient absenteeism risk.
This approach enables proactive interventions, optimizing resource allocation and improving care quality.
The methodology supports reduced operational inefficiencies and enhanced patient outcomes.