Prediction of absenteeism in medical appointments using Machine Learning

Abstract

The scheduling of medical appointments is an activity of great importance in a hospital since different human and material capitals must be used efficiently. One of the problems of this work is the non-attendance of a patient, which decreases the efficiency of the use of resources. Several studies have proposed considering "absenteeism" for scheduling medical appointments to address this. However, predicting it is a complex task. This research proposes the prediction of absenteeism to medical appointments for three medical areas of the Hospital Clínico Regional Dr. Guillermo Grant Benavente in the city of Concepción, Chile. For this purpose, five Machine Learning algorithms are trained and evaluated. The best-trained model managed to be a predictive tool of a patient's absenteeism level for his next appointment and to characterize those patients with higher levels of no-show.