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

Luiz Henrique A. Salazar, Valderi R. Q. Leithardt, Wemerson Delcio Parreira, Anita M. da Rocha Fernandes, Jorge Luis Victória Barbosa and Sérgio Duarte Correia
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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
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

Future Internet, 2021, vol. 14, issue 1, 1-21

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 (search for similar items in EconPapers)
JEL-codes: O3 (search for similar items in EconPapers)
Date: 2021
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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