  Patient no-shows is a major burden for health centers leading to loss of
revenue, increased waiting time and deteriorated health outcome. Developing
machine learning (ML) models for the prediction of no -shows could help
addressing this important issue. It is crucial to consider fair ML models for
no-show prediction in order to ensure equality of opportunity in accessing
healthcare services. In this wo rk, we are interested in developing deep
learning models for no-show prediction based on tabular data while ensuring
fairness properties. Our baseline model, TabNet, uses on attentive feature
transforme rs and has shown promising results for tabular data. We propose
Fair-TabNet based on representation learning that disentangles predictive from
sensitive components. The model is trained to jointly min imize loss functions
on no-shows and sensitive variables while ensuring that the sensitive and
prediction representations are orthogonal. In the experimental analysis, we
used a hospital dataset of 210, 000 appointments collected in 2019. Our
preliminary results show that the proposed Fair-TabNet improves the predictive,
fairness performance and convergence speed over TabNet for the task of
appointment no-show prediction. The comparison with the state-of-the art models
for tabular data shows promising results and could be further improved by a
better tuning of hyper-parameters.
