One of the important types of data in several applications is censored survival data processed in the framework of survival analysis [1, 2]. This type of data can be found in applications where objects are characterized by times to some events of interest, for example, by times to failure in reliability, times to recovery or times to death in medicine, times to bankruptcy of a bank or times to an economic crisis in economics. The important peculiarity of survival data is that the corresponding event does not necessarily occur during its observation period. In this case, we say about the so-called censored or right-censored data [3]. There are many machine learning models dealing with survival data, including models based on applying and extending the Cox proportional hazard model [4], for example, models presented in [5, 6], models based on a survival modification of random forests and called random survival forests (RSF) [7, 8, 9, 10, 11], models extending the neural networks [6, 12, 13, 14]. These models have gained considerable attention for their ability to analyze time-to-event data and to predict survival outcomes accurately. However, most models are perceived as black boxes, lacking interpretability.

A new modification of the Neural Additive Model (NAM) called SurvNAM and its modifications are proposed to explain predictions of the black-box machine learning survival model. The method is based on applying the original NAM to solving the explanation problem in the framework of survival analysis. The basic idea behind SurvNAM is to train the network by means of a specific expected loss function which takes into account peculiarities of the survival model predictions and is based on approximating the black-box model by the extension of the Cox proportional hazards model which uses the well-known Generalized Additive Model (GAM) in place of the simple linear relationship of covariates. The proposed method SurvNAM allows performing the local and global explanation. A set of examples around the explained example is randomly generated for the local explanation. The global explanation uses the whole training dataset. The proposed modifications of SurvNAM are based on using the Lasso-based regularization for functions from GAM and for a special representation of the GAM functions using their weighted linear and non-linear parts, which is implemented as a shortcut connection. A lot of numerical experiments illustrate the SurvNAM efficiency.