Use of machine learning in the forecast of clinical consequences of cancer diseases

Abstract

This paper is about existing problems of concerning precise forecasting of clinical consequences of a certain type of disease which are analyzed. It is shown that existing systems of machine learning are differently effective and have automated training, assessment and interpretation of deep learning models. The choice of software SurvivalNet (SN) that allows users to train and interpret deep survival models is substantiated.