## 5 Conclusions  
In this paper, we present the following results:  
* Dropping features using the RFE process improves the performance metrics. * Tree-based models have poor results with small data sets and provide little information on feature importance. * Optimization through reordering of features improves accuracy for most classifiers. * The ordering of the features for the LR and LDA classifiers improves after optimization, also highlighting possible related features. * Comparison of the results of the optimized LR and LDA models and the LSVC and GBC models after RFE provides meaningful information on feature importance and useful insights for further investigation. In summary, we can say that although the correlation graphs indicate that there is not enough information in the given data set to produce accurate regression models, using feature selection methods and comparing the results of several estimators can provide relevant insight in feature ranking for a data set with limited samples. Furthermore, the given optimization process almost always results in higher accuracy, which is useful when models are used for further predictive classification. However, when the focus lies on obtaining an understanding of which features influence the outcome of the classification, we see that feature selection, with or without the optimization, should be used with care. \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Feature (Average Ranking)** & **LSVC** & **GBC** & **LR** & **LDA** \\ \hline operation-type (1) & 1 & 1 & 1 (1) & 1 (1) \\
5 XCRT-T0 (7) & 6 & 3 & 8 (9) & 11 (14) \\ CSI-T1 (8.8) & 9 & 2 & 12 (12) & 12 (12) \\ prob-unipalid-days (9.8) & 5 & 9 & 10 (7) & 15 (13) \\ days-in-hospital (2.7) & 2 & & 3 (3) & 3 (3) \\ problems-caused-work (4) & 3 & & 5 (4) & 4 (4) \\ nr-of-days-off-work (9.7) & 7 & 7 & 14 (11) & 8 (8) \\ average-inactivity-post (10.7) & 12 & 10 & 10 (10) \\ WORK-T1 (15) & 11 & & 18 (14) & 16 (16) \\ type-of-work (2) & & & 2 (2) & 2 (2) \\ gender (4.5) & & & 4 (5) & 5 (5) \\ work-capable-wo-over (5.5) & 4 & & 7 (6) \\ breadwinner (7.5) & 8 & & & 7 (6) \\ CSI-T0 (10.5) & 10 & & 11 (13) & (17) \\ hours-per-week (12) & & 5 & 19 (24) \\
30 XR1-T1 (13.5) & & & 13 (15) & 14 (15) \\
30 XRT-T0 (17) & & & 21 & 13 (11) \\ KOOS-A-T0(22) & & & 22 & \\ KOOS-C-T1(23) & & & 23 & \\ \hline \hline \end{tabular}
\end{table} 
Table 6: Selected features for Linear Support Vector Classification (LSVC), Gradient Boost Classification (GBC),Optimized Logistic Regressor Classification(LR), and Optimized Linear Discriminant Analysis Classification (LDA). Original ranking after RFE for LR and LDA is given in brackets. Introducing a scoring value, such as the proposed \(S_{ordering}\), could prove to be very helpful in these cases. Conceptualization, H.H.R., P.C.-C. and M.T. ; methodology, H.H.R. and P.C.-C.; software, H.H.R. ; validation, D.O.S., P.C.-C., T.B.D., H.K.E.O. and M.T. ; formal analysis, H.H.R., T.B.D. and P.C.-C.; investigation, H.H.R. ; resources, D.O.S. ; data curation, H.H.R. and D.O.S. ; writing--original draft preparation, H.H.R. ; writing--review and editing, D.O.S., PC, T.B.D., H.K.E.O. and M.T. ; visualization, H.H.R. ; supervision, P.C.-C., H.K.E.O. and M.T. ; project administration, H.K.E.O. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The BAAS study has been approved by the Medical Ethical Committee of the Amsterdam UMC, location AMC (reference ID: W21_454 # 21.504) and the Medical Ethical Committees of the local hospitals Nij Smellinghe (NS, reference ID: 19888) and Elizabeth Tweesteden Ziekenhuis (ETZ, reference ID: L1429.2021). The study is in line with the Medical Treatment Agreement Act (in Dutch: Wet Geneeskundige Behandelovereenkomst, WGBO), the Data Protection Act (in Dutch: Wet bescherming persoonsgegevens, WBP, per 28 May 2018 the EU General Data Protection Regulation) and the Code of Conduct for Responsible Use (FEDERA).