# Recommendation Algorithm Based on Survival Action Rules
## 4 Experiments and Results  
The experiments were conducted on 19 datasets in a 10-fold stratified cross-validation mode. The detailed characteristics of the datasets are presented in Table 2. The minimum number of examples that the new rule must cover \(\mu\) was set to 30%, and the maximum percentage of examples that can be covered by both the source and target rules \(\xi\) was set to 10%. For simplicity and due to the large number of datasets, all attributes were treated as flexible. An extensive case study was carried out for two scenarios. The first involves the application of our method to a specific dataset. In this study, it was assumed that the values of all attributes can be changed (i.e., no stable attributes--whose values cannot be changed--were defined in the datasets). The second case study involves a comparative analysis between the recommendations of our method and those of a counterfactual explanation approach using a specific test example. The quality of the induced rules and recommendations was assessed comprehensively. Table 3 presents the quantitative characteristics of the induced rule sets. The column \(P_{i}\) (\(i\in\{0.05,0.01\}\)) shows the percentage of statistically significant induced rules. A rule is considered statistically significant if the difference between its source and target Kaplan-Meier curves is statistically significant, indicating that the rule suggests changes that could contribute to real changes in the survival of the examples covered by its premise. The log-rank test was used for this comparison. The analysis of Table 3 shows that the number of action rules generated is not large, averaging about 8 rules. For most datasets, the number of BETTER rules generated is greater than the number of WORSE rules, meaning that more rules define conditions that lead to an increase in the average survival time. On average, the rules contain 3 actions, \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Dataset** & \(|D|\) & \(|A|\) & **Miss** & **Cens** \\ \hline FD001 [61] & 100 & 24 & 0.00 & 19.00 \\ FD002 [61] & 260 & 24 & 0.00 & 19.69 \\ FD003 [61] & 100 & 24 & 0.00 & 20.00 \\ FD004 [61] & 249 & 24 & 0.00 & 19.76 \\ GBSG2 [62] & 686 & 8 & 0.00 & 56.41 \\ melanoma [63] & 205 & 7 & 0.00 & 65.37 \\ actg320 [64] & 1151 & 11 & 0.00 & 91.66 \\ bmt-ch [65] & 187 & 35 & 1.24 & 54.55 \\ follicle [66] & 541 & 4 & 0.00 & 35.67 \\ hd [66] & 865 & 6 & 0.00 & 50.75 \\ lung [67] & 1032 & 7 & 2.60 & 25.97 \\ maintenance [68] & 1000 & 3 & 0.00 & 19.00 \\ mgus [69] & 241 & 9 & 19.59 & 23.65 \\ pbc [70] & 418 & 17 & 14.54 & 61.48 \\ std [71] & 877 & 21 & 0.00 & 60.43 \\ uis [64] & 575 & 13 & 0.00 & 19.30 \\ whas1 [64] & 481 & 7 & 0.00 & 48.23 \\ whas500 [64] & 500 & 13 & 0.00 & 57.00 \\ zinc [72] & 431 & 55 & 57.17 & 81.21 \\ \hline \hline \end{tabular}
\end{table} 
Table 2: Description of datasets employed in experimental research. Columns are as follows: the dataset name and source, the number of examples (\(|D|\)), the number of conditional attributes (\(|A|\)), the percentage of missing values (Miss), and the percentage of censored observations in the set (Cens). with 2 to 3 of these actions being different from ANY actions, suggesting specific changes in attribute values. There is a notable variation in the coverage of source and target rules across the datasets. For example, actg320 shows a significant difference in coverage between source and target (22.48% vs. 39.29%), indicating a substantial extension of the applicability of the rules from source to target. For the two datasets, \"std\" and \"lung\", the average number of \"WORSE\" type rules exceeded the number of \"BETTER\" type rules. WORSE type rules give information about what actions to pay attention to in order not to worsen survival time. Depending on the needs, the algorithm can be parameterized to obtain only BETTER type rules, only WORSE type rules, or rules of any type, as in the considered scenario.