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

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-sh...

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Bibliographic Details
Published in:Future internet 2022-01, Vol.14 (1), p.3
Main Authors: Salazar, Luiz Henrique A., Leithardt, Valderi R. Q., Parreira, Wemerson Delcio, da Rocha Fernandes, Anita M., Barbosa, Jorge Luis Victória, Correia, Sérgio Duarte
Format: Article
Language:English
Subjects:
Absenteeism
Algorithms
Artificial intelligence
Data analysis
Data science
Datasets
Health care policy
Health facilities
Health services
healthcare applications
Internet
Machine learning
Medical appointments
patient attitudes
Patients
Prediction models
Public health
Scheduling
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Summary: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.
ISSN:1999-5903
1999-5903
DOI:10.3390/fi14010003