###### Abstract
For this purpose, various measurements and data that can be obtained preoperatively are used, such as the number of resected lung segments, quantitative computed tomography (CT), spirometry, or perfusion scintigraphy [4; 6; 7; 8]. The common feature of all these methods is that they are tested for a prediction horizon of three to six months after lung resection [4]. Contrary to such predictions, ppoFEV1 for a time horizon of up to seven days would represent a key indicator of the surgery outcome since most cardio-respiratory complications are developed during that period. At the same time, that period coincides with the typical length of the patient's stay in the hospital, when various measures can be taken to reduce the risk of an undesirable outcome. Unfortunately, in the available literature, there are very few results related to this immediate postoperative period. In the paper [9], it was stated that the existing methods for calculating predicted poFEV1 significantly underestimate the loss of lung function in the immediate postoperative period of 6 days. From these facts, the basic motivation and goal of our work follows: the synthesis of a machine learning (ML) system for predicting postoperative FEV1 within the first 7 days after undergoing pulmonary resection, individually for each patient. For this study, we collected necessary measurements and data from 79 patients who underwent pulmonary resection. Each patient is described with an initial 35 features, which include the patient's age, type of operation, cancer location, various spirometry measurements, the number of lung segments to be removed, as well as a series of measurements related to the mobility of the left and right hemidiaphragm, etc. In order to be included in the study, the presence of primary cancer was also necessary, as well as a complete assessment of the functional status and overall cardiorespiratory risk. The proposed machine learning system is especially trained to predict postoperative FEV1 for the 1 st, 4 th, and 7 th day after surgery. To our knowledge, this represents the first published result that predicts postoperative FEV1 in the most critical postoperative period of the first 7 days. The high accuracy of the proposed predictive meta model, with a mean absolute error (MAE) ranging from 8% to 11% and mean absolute percentage error (MAPE) ranging from 14% to 23%, provides surgeons with a number of key insights, starting with whether the patient is suitable for surgical intervention, and ending with the preparation of individual postoperative treatment. A considerable benefit for clinical practice is the doctor's precise insight into the impact of each input feature on the resulting ppoFEV1 individually for each patient before surgery. This insight can be obtained by calculating SHAP values based on the obtained regression model [10; 11]. The additive nature of SHAP values and simple interpretation, in our opinion, provide great opportunities for significant improvement of the clinical practice of lung cancer treatment, especially in terms of more precise preoperative insight into possible risks, as well as in the personalization of the recovery procedure. It should be kept in mind that the validity of using SHAP values for these purposes is critically conditioned by the accuracy of the given predictive model. Preliminary results of our research intended for medical circles, were published earlier [12]. The central question analyzed in [12] was the confirmation of the significant influence of inspiratory respiration muscles on postoperative prediction of the lung function in the first seven postoperative days. The goal of this paper is to provide the machine learning community with our results obtained during the development of the system [12]. The rest of this paper is organized as follows. In Section 2, the synthesis procedure of the regression prediction model for postoperative FEV1 is given. In order to achieve the highest possible accuracy, it was necessary to complicate the architecture of the predictor.