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Future Internet
Volume 14
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10.3390/fi14010003
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Article

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

by
Luiz Henrique A. Salazar
1,
Valderi R. Q. Leithardt
2,3,*,
Wemerson Delcio Parreira
1,*,
Anita M. da Rocha Fernandes
1,*,
Jorge Luis Victória Barbosa
4 and
Sérgio Duarte Correia
2,3
1
Laboratory of Embedded and Distributed Systems, University of Vale do Itajai, Itajai 88302-901, Brazil
2
VALORIZA, Research Center for Endogenous Resources Valorization, Instituto Politécnico de Portalegre, 7300-555 Portalegre, Portugal
3
COPELABS, Universidade Lusófona de Humanidades e Tecnologias, 1749-024 Lisbon, Portugal
4
Applied Computing Graduate Program, University of Vale do Rio dos Sinos, Av. Unisinos 950, Bairro Cristo Rei, Sao Leopoldo 93022-750, Brazil
*
Authors to whom correspondence should be addressed.
Future Internet 2022, 14(1), 3; https://doi.org/10.3390/fi14010003
Submission received: 26 November 2021 / Revised: 17 December 2021 / Accepted: 20 December 2021 / Published: 22 December 2021
(This article belongs to the Special Issue Smart Objects and Technologies for Social Good)
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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.
Keywords: artificial intelligence; data science; healthcare applications; machine learning; patient attitudes artificial intelligence; data science; healthcare applications; machine learning; patient attitudes

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MDPI and ACS Style

Salazar, L.H.A.; Leithardt, V.R.Q.; Parreira, W.D.; da Rocha Fernandes, A.M.; Barbosa, J.L.V.; Correia, S.D. Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector. Future Internet 2022, 14, 3. https://doi.org/10.3390/fi14010003

AMA Style

Salazar LHA, Leithardt VRQ, Parreira WD, da Rocha Fernandes AM, Barbosa JLV, Correia SD. Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector. Future Internet. 2022; 14(1):3. https://doi.org/10.3390/fi14010003

Chicago/Turabian Style

Salazar, Luiz Henrique A., Valderi R. Q. Leithardt, Wemerson Delcio Parreira, Anita M. da Rocha Fernandes, Jorge Luis Victória Barbosa, and Sérgio Duarte Correia. 2022. "Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector" Future Internet 14, no. 1: 3. https://doi.org/10.3390/fi14010003

APA Style

Salazar, L. H. A., Leithardt, V. R. Q., Parreira, W. D., da Rocha Fernandes, A. M., Barbosa, J. L. V., & Correia, S. D. (2022). Application of Machine Learning Techniques to Predict a Patient’s No-Show in the Healthcare Sector. Future Internet, 14(1), 3. https://doi.org/10.3390/fi14010003

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