  In this paper, we consider survival analysis with right-censored data which
is a common situation in predictive maintenance and health field. We propose a
model based on the estimation of two-parameter Weibull distribution
conditionally to the features. To achieve this result, we describe a neural
network architecture and the associated loss functions that takes into account
the right-censored data. We extend the approach to a finite mixture of
two-parameter Weibull distributions. We first validate that our model is able
to precisely estimate the right parameters of the conditional Weibull
distribution on synthetic datasets. In numerical experiments on two real-word
datasets (METABRIC and SEER), our model outperforms the state-of-the-art
methods. We also demonstrate that our approach can consider any survival time
horizon.
