Modelling survival by machine learning methods in liver transplantation: application to the UNOS dataset


The aim of this study is to develop and validate a machine learning (ML) model for predicting survival after liver transplantation based on pre-transplant donor and recipient characteristics. For this pur- pose, we consider a database from the United Network for Organ Shar- ing (UNOS), containing 29 variables and 39,095 donor-recipient pairs, describing liver transplantations performed in the United States of Amer- ica from November 2004 until June 2015. The dataset contains more than a 74% of censoring, being a challenging and difficult problem. Sev- eral methods including proportional-hazards regression models and ML methods such as Gradient Boosting were applied, using 10 donor char- acteristics, 15 recipient characteristics and 4 shared variables associated with the donor-recipient pair. In order to measure the performance of the seven state-of-the-art methodologies, three different evaluation met- rics are used, being the concordance index (ipcw) the most suitable for this problem. The results achieved show that, for each measure, a dif- ferent technique obtains the highest value, performing almost the same, but, if we focus on ipcw, Gradient Boosting outperforms the rest of the methods.

Proceedings of the 20th International Conference on Intelligent Data Engineering and Automated Learning (IDEAL2019)