Random forest

Demography

Cognitive psychology

Engineering

Health care

Recall rate

Classifier (UML)

Computer science

Recall

Environmental health

Internet privacy

Mechanical engineering

Operations research

Psychology

Health services

Health sector

Political science

Work (physics)

Population

Sociology

Artificial intelligence

Patient data

Medicine

Machine learning

Future Internet

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.

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