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; Sérgio Duarte Correia

doi:10.3390/fi14010003

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, Sérgio Duarte Correia

COPELABS - Centro de Investigação em Computação Centrada nas Pessoas e Cognição

Resultado de pesquisa › revisão de pares

32 Citações (Scopus)

Resumo

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.

Idioma original	Inglês
Número do artigo	3
Revista	Future Internet
Volume	14
Número de emissão	1
DOIs	https://doi.org/10.3390/fi14010003
Estado da publicação	Publicadas - jan. 2022

Nota bibliográfica

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.

Financiamento

Financiadoras/-es	Número do financiador
Aperfei?oamento de Pessoal de N?vel Superior
Ci?ncia e a Tecnologia
EDITAL DE CHAMADA PÚBLICA FAPESC	06/2017
Instituto Lusófono de Investigação e Desenvolvimento
Pesquisa do Estado de Santa Catarina
UIDB/05064/2020
VALORIZA-Research Center for Endogenous Resource Valorization
Fundação para a Ciência e a Tecnologia	UIDB/04111/2020
Coordenação de Aperfeiçoamento de Pessoal de Nível Superior	001
Fundação de Amparo à Pesquisa e Inovação do Estado de Santa Catarina	COFAC/ILIND/COPELABS/3/2020, CO-FAC/ILIND/COPELABS/1/2020

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10.3390/fi14010003

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Citar isto

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title = "Application of machine learning techniques to predict a patient{\textquoteright}s no-show in the healthcare sector",

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{\textquoteright}s profile and predict potential absences. The goal of this work is to explore the main causes that contribute to a patient{\textquoteright}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 Itaja{\'i} 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 Itaja{\'i}, under opinion 4270,234, contemplating the General Data Protection Law.",

keywords = "Artificial intelligence, Data science, Healthcare applications, Machine learning, Patient attitudes",

author = "Salazar, {Luiz Henrique A.} and Leithardt, {Valderi R.Q.} and Parreira, {Wemerson Delcio} and {da Rocha Fernandes}, {Anita M.} and Barbosa, {Jorge Luis Vict{\'o}ria} and Correia, {S{\'e}rgio Duarte}",

note = "Publisher Copyright: {\textcopyright} 2021 by the authors. Licensee MDPI, Basel, Switzerland.",

year = "2022",

month = jan,

doi = "10.3390/fi14010003",

language = "English",

volume = "14",

journal = "Future Internet",

issn = "1999-5903",

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number = "1",

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AU - Salazar, Luiz Henrique A.

AU - Leithardt, Valderi R.Q.

AU - Parreira, Wemerson Delcio

AU - da Rocha Fernandes, Anita M.

AU - Barbosa, Jorge Luis Victória

AU - Correia, Sérgio Duarte

PY - 2022/1

Y1 - 2022/1

N2 - 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.

AB - 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.

KW - Artificial intelligence

KW - Data science

KW - Healthcare applications

KW - Machine learning

KW - Patient attitudes

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Application of machine learning techniques to predict a patient’s no-show in the healthcare sector

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