# Recommendation Algorithm Based on Survival Action Rules
## 5 Conclusions  
The article presents a recommendation algorithm based on survival action rules. The recommendation algorithm is based on previously induced survival action rules. The motivation for the development of this algorithm arises from situations where decisions about changing the values of a specific example are complicated by the fact that the example is covered by numerous action rules. The recommendation algorithm, which relies on the induction of action rules in a specially prepared meta-table, addresses this issue. The meta-table represents a kind of discretization for continuous attributes, as defined by the previously determined action rules.  
The experiments conducted show that the algorithm is a useful tool for generating recommendations. The presented algorithm is the first of its kind designed for censored data. Survival action rules and the recommendation algorithm based on them offer a slightly different solution than the counterfactual explanations (CF) methodology. To our knowledge, there are at least two implementations of CF dedicated to censored data. However, CF is a method that works locally to identify the minimal changes required for the estimated survival time of a test example to differ from the currently estimated time. The action rule induction algorithm generates a model of value changes for the entire training dataset, and then the recommendation algorithm generates change recommendations for new (or training) examples. The recommendation algorithm also looks for changes that cause the most significant change in the estimated survival time.  
The algorithm is available in the GitHub repository ([[https://github.com/adaa-polsl/action-mining-based-on-sar](https://github.com/adaa-polsl/action-mining-based-on-sar)]([https://github.com/adaa-polsl/action-mining-based-on-sar](https://github.com/adaa-polsl/action-mining-based-on-sar)), accessed on 26 March 2024) along with detailed results from the conducted experiments.  
Our further work will focus on the development of an algorithm for the induction of exception rules [75] tailored to the task of action planning. Exception rules allow the identification of specific actions within induced action rules (source rules) whose application does not positively affect the reassignment of an example to the intended target class. The identification of such rules is expected to result in an action model that provides more precise recommendations. We also aim to integrate software that enables the induction of action rules with the RuleKit package [76] we are developing, further enhancing its capabilities.  
Conceptualization, M.S.; methodology, M.S. and M.H.; software, M.H.; validation, M.H. and M.S.; formal analysis, M.H.; investigation, M.H., B.S., M.S. and L.W.; resources,  
Figure 7: Kaplan–Meier plots for three samples: the original test example \(x\), the example \(x\) after recommendation-based mutation, and a counterfactual for \(x\).  
M.H. and B.S.; data curation, M.H. and B.S.; writing--original draft preparation, M.S., M.H. and B.S.; writing--review and editing, M.S., B.S. and L.W.; visualization, M.H.; supervision, M.S.; project administration, B.S. and L.W.; funding acquisition, L.W. All authors have read and agreed to the published version of the manuscript.  
This work was partially supported by the Polish National Centre for Research and Development, Poland, under the Operational Program Intelligent Development (grant POIR.01.01.01-00-0304/19). The publication was supported by the rector's grant for scientific quality improvement. Silesian University of Technology, grant number: 02/120/RGJ23/0028 and 02/120/RGJ23/0027.  
The original data presented in the study are openly available. For dataset access and relevant literature, see Table 2.  
The authors declare no conflicts of interest.  
The following abbreviations are used in this manuscript:  
BMI Body Mass Index counterfactual explanations Kolar-Meier survival action rules