  Low attendance levels in medical appointments have been associated with poor
health outcomes and efficiency problems for service providers. To address this
problem, healthcare managers could aim at improving attendance levels or
minimizing the operational impact of no-shows by adapting resource allocation
policies. However, given the uncertainty of patient behaviour, generating
relevant information regarding no-show probabilities could support the
decision-making process for both approaches. In this context many researchers
have used multiple regression models to identify patient and appointment
characteristics than can be used as good predictors for no-show probabilities.
This work develops a Decision Support System (DSS) to support the
implementation of strategies to encourage attendance, for a preventive care
program targeted at underserved communities in Bogot\'a, Colombia. Our
contribution to literature is threefold. Firstly, we assess the effectiveness
of different machine learning approaches to improve the accuracy of regression
models. In particular, Random Forest and Neural Networks are used to model the
problem accounting for non-linearity and variable interactions. Secondly, we
propose a novel use of Layer-wise Relevance Propagation in order to improve the
explainability of neural network predictions and obtain insights from the
modelling step. Thirdly, we identify variables explaining no-show probabilities
in a developing context and study its policy implications and potential for
improving healthcare access. In addition to quantifying relationships reported
in previous studies, we find that income and neighbourhood crime statistics
affect no-show probabilities. Our results will support patient prioritization
in a pilot behavioural intervention and will inform appointment planning
decisions.
