## 4 Technical Contribution  
Given the fact that our knowledge base has been curated with the support of experienced surgeons, we have no missing data in the training set, and therefore, no data imputation that can further increase the bias, and the data drift was necessary. We only had to encode the data since some features are categorical and other features are numerical, and for them, normalization was performed by subtracting the mean and dividing by the standard deviation. Another problem was related to the imbalance of the dataset. As can be observed from Figure 4, we have more cases with cancer than with no cancer. To solve this issue, we have used the SMOTE technique to balance the dataset [5]. SMOTE (Synthetic Minority Oversampling Technique) consists of synthesizing elements for the minority class based on those that already exist. It works randomly by picking a point from the minority class and computing the k-nearest neighbors for this point. The synthetic points are added between the chosen point and its neighbors. The SMOTE provided a number of subscriptions, 37, and the proportion of no subscription data in the oversampled data was 0.5, and the proportion of subscription data in the oversampled data was 0.5, giving us a perfectly balanced set, as in Figure 4b. The input of the AI-based system (see Figure 2) is represented by the independent clinical parameters described in Section 2 plus the dependent variable, ExamHP, which is our target. The ExamHP parameter represents the value of the histopathological examination and stands as the ground truth for training our system. As said before, the data were collected from 150 patients and then curated by experienced surgeons in order to benefit from the advantages of an expert system. Moreover, to have representative results, we trained the ensemble prediction model with various combinations of parameters, as will be explained below. For a global observation of the collected data, we first computed a statistical analysis figure (see Figure 5 below), which considers the minimum and maximum values, and calculates the standard deviation, which allows us to immediately observe the values that are out of the medium range. The percentiles that appear in the figure are to be included in the output. All of the values should fall between 0 and 1. The defaults are [0.25, 0.5, and 0.75], which return the 25 th, 50 th, and 75 th percentiles. For the numeric data, the results will include count, mean, std, min, max, as well as the lower 50 and upper percentiles. By default, the lower percentile is 25, and the upper percentile is 75. The 50 percentile is the same as the median. The importance of deciding on the necessity of surgery due to lung cancer disease is different from a lot of other types of cancer due to the fact that biopsy can be prevented only  
Figure 4: (**a**) imbalanced dataset, (**b**) SMOTE-based balanced dataset. Figure 5: Statistical analysis of the dataset (in Figures 2 and 5, the columns entitled ‘semo011’ and ‘semo012’ correspond to halo sign—referring to part solid ground-glass opacity—and feeding blood vessel sign, respectively). during surgery, and if the patient does not have cancer, then this procedure would not have been necessary. Combining the experience of the medical doctor with the data provided by automatic data processing can achieve the desired output. The pipeline approach for automatic data processing by using machine learning consists of designing the ensemble model, loading the data, training the model, and then interpreting the inference and explaining the prediction. For someone without a technical background, such as a surgeon, the explainability part could be most helpful. Moreover, in addition to the actual model designed exactly to best fit this purpose, we also applied a covariant function on the data, additionally computed alongside the model, to obtain a features correlation matrix. We can see this matrix in Figure 6. This image also offers a global perspective on the correlation between the data.