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Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector

Author

Listed:
  • Luiz Henrique A. Salazar

    (Laboratory of Embedded and Distributed Systems, University of Vale do Itajai, Itajai 88302-901, Brazil)

  • Valderi R. Q. Leithardt

    (VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
    COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal)

  • Wemerson Delcio Parreira

    (Laboratory of Embedded and Distributed Systems, University of Vale do Itajai, Itajai 88302-901, Brazil)

  • Anita M. da Rocha Fernandes

    (Laboratory of Embedded and Distributed Systems, University of Vale do Itajai, Itajai 88302-901, Brazil)

  • Jorge Luis Victória Barbosa

    (Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, Bairro Cristo Rei, Sao Leopoldo 93022-750, Brazil)

  • Sérgio Duarte Correia

    (VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
    COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal)

Abstract

The health sector faces a series of problems generated by patients who miss their scheduled appointments. The main challenge to this problem is to understand the patient’s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient’s no-show and develop a prediction model able to identify whether the patient will attend their scheduled appointment or not. The study was based on data from clinics that serve the Unified Health System (SUS) at the University of Vale do Itajaí in southern Brazil. The model obtained was tested on a real collected dataset with about 5000 samples. The best model result was performed by the Random Forest classifier. It had the best Recall Rate (0.91) and achieved an ROC curve rate of 0.969. This research was approved and authorized by the Ethics Committee of the University of Vale do Itajaí, under opinion 4270,234, contemplating the General Data Protection Law.

Suggested Citation

  • Luiz Henrique A. Salazar & Valderi R. Q. Leithardt & Wemerson Delcio Parreira & Anita M. da Rocha Fernandes & Jorge Luis Victória Barbosa & Sérgio Duarte Correia, 2021. "Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector," Future Internet, MDPI, vol. 14(1), pages 1-21, December.
    • Handle: RePEc:gam:jftint:v:14:y:2021:i:1:p:3-:d:708193
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    Cited by:

    1. Ganjar Alfian & Muhammad Syafrudin & Norma Latif Fitriyani & Sahirul Alam & Dinar Nugroho Pratomo & Lukman Subekti & Muhammad Qois Huzyan Octava & Ninis Dyah Yulianingsih & Fransiskus Tatas Dwi Atmaji, 2023. "Utilizing Random Forest with iForest-Based Outlier Detection and SMOTE to Detect Movement and Direction of RFID Tags," Future Internet, MDPI, vol. 15(3), pages 1-16, March.

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