## 2 Materials and Methods
The base value of FEV1% = 44.871 at the bottom, is the average ML output for all observations. The model prediction for this patient is FEV1% = 62.723, as shown at the top. patient n1 are shown. Figure 12 clearly shows that patient n1 will have a critically low FEV1% = 34,467 on the first day after surgery, primarily because his preoperative values for T, V, X, and P are quite low. For patient n2 from Figure 13, a rather high value of FEV1% = 62,723 is predicted on the first postoperative day. This predicted outcome is the result of high preoperative values for critical features T, V, CY, X, and GB. It is interesting to note that among these features is the feature CY (Mobility of the right hemidiaphragm measured radiographically), which also belongs to the composite feature IRM (inspiratory respiration muscles). As we have already established, IRM is the most responsible for the postoperative outcome on the first day after surgery. ## 5 Conclusions

The presented experimental results and the interpretation of the identified regression meta-model for predicting the postoperative condition of patients during the first week after lung cancer surgery imply the following facts:

1. It is possible for these purposes to design a multi-layer prognostic regression meta-model with sufficient accuracy even in the conditions of relatively small training sets with input features that are routinely collected in the preoperative period. 2. The accuracy of the proposed model far exceeds the accuracy of traditional prognostic models. 3. Global interpretation of the obtained meta-model using SHAP values showed several interesting new insights important for clinical practice, such as the role of IRM and BMI on the condition of patients on the first critical day after surgery. 4. It was demonstrated how, based on local interpretation of SHAP values, a more accurate picture of postoperative risk factors personalized for each patient is obtained. This interpretation is performed preoperatively, which in our opinion can contribute to a significant improvement of the surgery procedure itself, as well as more successful postoperative rehabilitation of patients. The introduction of such consultative systems into clinical practice is associated with a number of additional production challenges, such as continuous updating of training sets, adaptation of the system to current changes in the health status of the relevant population, as well as continuous education of doctors in accepting this new technology. Conceptualization, R.V. and M.M. ; methodology, M.M. and J.R.; software, J.R.; validation, R.V., M.M. and J.R.; formal analysis, R.V. and M.M. ; investigation, R.V. ; resources, R.V. ; data curation, R.V. ; writing--original draft preparation, R.V. ; writing--review and editing, M.M. and M.P. ; visualization, J.R.; supervision, M.M. and M.P. ; project administration, M.M. and M.P. ; funding acquisition, M.M. All authors have read and agreed to the published version of the manuscript. This research received no external funding. The study was conducted in accordance with the World Medical Association Declaration of Helsinki and in accordance with the relevant guidelines and regulations. This study protocol was reviewed and approved by the Ethics Committee of the University of Belgrade, Faculty of Medicine, approval number 29/XII-10. Informed Consent Statement: Informed consent was obtained from all subjects involved in the study. The datasets used and/or analyzed in the current study are available through the corresponding author. The data are not publicly available due to privacy. This work was supported by the Vlatacom Institute of High Technologies. The authors declare no conflicts of interest. ## References

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