Generalized additive neural network with flexible parametric link function: model estimation using simulated and real clinical data

Artificial neural networks have been proposed
in medical research as an alternative to some regression
models such as the generalized linear models, being the
multilayer perceptron (MLP) the most used architecture.
However, inspired in the generalized additive models
(GAM), recent studies proposed the more transparent
generalized additive neural network (GANN) architecture.
In fact, while a MLP may be seen as a black box in which
the effect of a variable on the outcome is not clear, a
GANN has the advantage of being able to study objectively,
through a graphical approach, the effect of an input
variable on a certain outcome of interest [9]. In this study,
the GANN’s architecture was updated, considering some
features already available in the GAM, namely the use of a
flexible parametric link function based on the Aranda-Ordaz
transformations family for a binary response. Also, the
interpretability was improved by obtaining the partial
functions with the corresponding confidence intervals
through the bootstrap method. The performance of the
proposed model was evaluated with simulated data and
further applied to a real clinical dataset.