# Recommendation Algorithm Based on Survival Action Rules
###### Abstract  
Survival analysis is widely used in fields such as medical research and reliability engineering to analyze data where not all subjects experience the event of interest by the end of the study. It requires dedicated methods capable of handling censored cases. This paper extends the collection of techniques applicable to censored data by introducing a novel algorithm for interpretable recommendations based on a set of survival action rules. Each action rule contains recommendations for changing the values of attributes describing examples. As a result of applying the action rules, an example is moved from a group characterized by a survival curve to another group with a significantly different survival rate. In practice, an example can be covered by several induced rules. To decide which attribute values should be changed, we propose a recommendation algorithm that analyzes all actions suggested by the rules covering the example. The efficiency of the algorithm has been evaluated on several benchmark datasets. We also present a qualitative analysis of the generated recommendations through a case study. The results indicate that the proposed method produces high-quality recommendations and leads to a significant change in the estimated survival time. recommendations; survival analysis; survival action rules +
Footnote †: journal: Medical  
attribute space where there are examples with a significantly different survival rate than the considered example before the changes. If each example were covered by only one rule, the procedure would be straightforward: simply make the changes recommended by the rule applicable to that particular example. In practice, however, examples are covered by many rules, sometimes containing conflicting recommendations. This scenario represents one of the main scientific problems addressed in this paper. Consequently, there is a need to develop a method for resolving conflicts between rules, similar to how conflicts are resolved in classification systems. This article presents a proposal for an algorithm for generating recommendations based on a set of survival action rules. The main contribution of this article is the proposal of such an algorithm. Based on the induced survival rules, the algorithm builds a special meta-table containing information about all elementary conditions (elementary actions) that constitute the induced rules. In this meta-table, conflict resolution between rules covering the example is solved by inducing a special meta-action survival rule. The meta-action survival rule contains the actions that maximally differentiate the survival rates, characterizing the example before and after the application of these actions. The efficiency of the algorithm was evaluated on several benchmark datasets. In addition, we present a qualitative analysis of the generated recommendations through a case study. The recommendation algorithm introduced in this study can be considered to be a specialized counterfactual explanation method tailored for survival data analysis. So far, only one counterfactual explanation method dedicated to survival data has been proposed [3]. Furthermore, this method operates with datasets that exclusively contain numerical attributes. It generates recommendations for a specific instance without generating a global action model. The recommendation algorithm presented in the article has several unique features:  
* it is based on a global action model represented by survival action rules,
* recommendations for a specific instance are generated based on the fusion of information contained in the action model,
* the method can be applied to datasets containing numerical as well as categorical attributes,
* the algorithm uses a computationally efficient hill climbing strategy to search recommendations. The article also presents a proposal for verifying the quality of the recommendation algorithm. The proposed verification method does not require observing an example after the recommendations have been applied.