###### Abstract  
The goal of this paper is to inform the machine learning community of our results obtained during the development of a system for the assessment of the postoperative lung function of patients suffering from lung cancer. The system is based on a new multilayer regression meta-model, which predicts individual postoperative forced expiratory volume in 1 second (poFEV1) for each patient based on preoperative measurements. The proposed regression models are especially trained to predict this key indicator for the 1 st, 4 th, and 7 th day after surgery. Based on our knowledge, this is the first attempt to obtain poFEV1 in the most critical postoperative period of the first seven days. The high accuracy of the proposed predictive meta-model allows surgeons a number of key insights, starting with whether the patient is suitable for surgical intervention, and ending with the preparation of individualized postoperative treatment. It should be noted that the existing, widely used predictive models, based on functional segments (FC), Juhl-Forst, and Nakahara formulas, give two to three times worse results compared to the proposed new regression meta-model. Based on the SHapley Additive explanations (SHAP) value of the trained meta-model, it is possible to obtain a complete picture of the partial effects of each prognostic factor for each patient preoperatively on the outcome of the surgical intervention. In addition, the global model interpretation by SHAP values reveals some new interdependencies that were not known in medical circles until now. For instance, the influence of age and biomass index on the condition of the patient on the first day after surgery, or the constant significant influence of muscular support for inhalation in the entire seven-day follow-up period. machine learning; stacked learning; forced expiratory volume; SHAP; personalized treatment +
Footnote †: journal: Medical  
4. Predicting Postoperative Outcomes: This information helps patients prepare for the recovery process and facilitates appropriate postoperative care planning. 5. Risk-Benefit Evaluation: For some patients with compromised lung function, the risks associated with surgery may outweigh the potential benefits. Postoperative lung function assessment helps to make informed decisions regarding the appropriateness of surgery as a treatment option for individual patients. 6. Postoperative Care Planning: Knowing the postoperative lung function enables healthcare providers to plan for appropriate postoperative care. It helps determine the need for intensive care, ventilatory support, or physiotherapy during the recovery period. In summary, the preoperative assessment of postoperative lung function is essential to identify high-risk patients, optimize surgical planning, set realistic expectations, and provide appropriate care before and after pulmonary resection. This approach improves patient safety, enhances outcomes, and helps ensure that the surgical procedure is performed with the highest level of personalized care [3; 4]. Forced Expiratory Volume in 1 s (FEV1) is a measure widely used in pulmonary function tests to assess lung function [4]. The test involves taking a deep breath in and then exhaling as quickly and forcefully as possible to measure the volume of air expelled within that initial one-second time frame. FEV1 was expressed in liters [L] or as a percentage [%] of the predicted value for age, gender, and height, according to the European Community for Steel and Coal prediction equations [5]. It is essential to note that FEV1 is just one component of pulmonary function testing, and additional parameters like Forced Vital Capacity (FVC) and the FEV1%/FVC ratio are often considered together to gain a comprehensive understanding of lung health. The predicted postoperative FEV1 (ppoFEV1) is today dominantly used in the assessment of postoperative lung function after pulmonary resection of patients suffering from lung cancer.