## 4 Discussion  
The results presented in Section 3 show that using the RFE selector can give some useful insights in the feature importance for a data set with limited sample numbers such as the one used in this study. Comparing the results of the fitted estimator in the test set to that of the training set, but also simply the development of the accuracy results of the estimator with each added feature, showed that for tree-based models, the RFE selection process is not performed optimally (see Figure 5) when looking at the ordering of features. Linear classification models, on the other hand, display an accuracy profile that is more like the expected convex upward function. Furthermore, although the proposed optimization process drastically improved the performance of the AdaBoost Classifier, the resulting graph showed that the order of the features was still not optimal. For the LDA, LR, LSVC and GBC, we saw that despite improved performance, the optimization only resulted in more meaningful ordering for the LDA and LR classifiers. Taking these observations into consideration, a discussion of the feature importance should be based on the results obtained from the LSVC, GBC after the RFE selection, and from the LR and LDA estimators after the optimization. The resulting features are listed in Table 6 in the same order as in Table 2, with the original ranking of the LR and LDA models given in brackets. Looking at the features that appear in these models, it is clear that the most important feature is the type of operation. Together with the fact that the length of stay in the hospital (_days-in-hospital_) ranks fifth, this shows that the impact of the operation is very determinant for the length of the RTW period. Logically, total KA has more impact than unicompartmental KA, and the length of stay in most cases depends on the recovery of the operation. A second interesting point is the fact that two of the higher ranking features reflect personal experiences of the rehabilitation process, namely the _CSI-T1_ and _WORK-T1_ features. This indicates the importance of the mental factor in the duration of the RTW period. However, it does not provide any information on whether the experience of the patients is the cause of the longer rehabilitation process, or if actual slow recovery is the cause of the patient's experience. This should be investigated by looking at individual results and, for example, the correlation with the values of _prob-unpaid-days_, _problems-caused-work_ and _type-of-work_, which are features that could provide more information on the patient's pain experience in relation to work. One feature that is of particular interest is the average inactivity post-operation. As can be seen from the positive coefficient, a higher number of inactivity moments in the first six weeks leads to a higher RTW value. This is a feature that could provide input for coaching during the rehabilitation process [23]. However, more research is needed to determine what causes the higher number; this could, for example, be caused by an overload of movements before the inactivity moments. \begin{table}
\begin{tabular}{l c c} \hline
**Classifier** & \(S_{ordering}\) **after RFE** & \(S_{ordering}\) **after Optimization** \\ \hline Linear SVC & 0.60 & 0.19 \\ Logistic Regression & 0.46 & 0.60 \\ Linear Discriminant Analysis & 0.37 & 0.57 \\ Gradient Boosting Classifier & 0.53 & 0.35 \\ AdaBoost Classifier & 0.53 & n/a \\ Random Forest Classifier & 0.31 & n/a \\ Decision Tree Classifier & 0.28 & n/a \\ \hline \end{tabular}
\end{table} 
Table 5: Results for \(S_{ordering}\). The final feature on the list of highest ranking features to be discussed is the _5 XCRT-T0_ feature. This value reflects the patient's preoperative ability to perform five sit-and-stand movements without using their hands. As such, it probably has a high correlation with the type of operation, which would explain the high ranking. This also needs to be investigated further.