Title: A supervised learning model for medical appointments no-show management

Authors: Inês Ferreira; André Vasconcelos

Addresses: INESC-ID, Instituto Superior Técnico, R. Alves Redol 9, 1000-029 Lisbon, Portugal ' INESC-ID, Instituto Superior Técnico, R. Alves Redol 9, 1000-029 Lisbon, Portugal

Abstract: A no-show is a phenomenon that leads to an efficiency decrease in various sectors, including in the healthcare sector. This research proposes the usage of supervised learning techniques to predict medical appointments no-shows occurrence and to find patient replacements to fulfil last-minute vacancy slots. The prediction is performed using a classification algorithm that computes the probability of no-show for each patient based on features that have shown to influence his or her decision, such as the waiting time, the day of the appointment and the number of previous no-shows, among others. The features are extracted from two distinct healthcare datasets. In order to select the most suitable classification algorithm, a ten-fold cross-validation is used to perform a comparative analysis among the most used algorithms applicable to this type of classification problems. The gradient boosting algorithm proved to have the best performance in estimating no-shows.

Keywords: no-show; healthcare; supervised learning; classification algorithms; cross-validation.

DOI: 10.1504/IJMEI.2022.119315

International Journal of Medical Engineering and Informatics, 2022 Vol.14 No.1, pp.90 - 104

Received: 05 Nov 2019
Accepted: 30 May 2020
Published online: 01 Dec 2021 *

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