# Explainable Machine Learning for Lung Cancer Screening Models  
[PERSON]  
1 Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland; [EMAIL_ADDRESS]  
[PERSON]  
2 National Institute of Tuberculosis and Lung Diseases, 01-138 Warsaw, Poland; [EMAIL_ADDRESS]  
[PERSON]  
3 Faculty of Medicine and Dentistry, Medical University of Silesia, 40-055 Katowice, Poland; [EMAIL_ADDRESS]  
[PERSON]  
1 Faculty of Mathematics, Informatics and Mechanics, University of Warsaw, 00-927 Warsaw, Poland; [EMAIL_ADDRESS]  
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as well as many software libraries for R or Python, such as DALEX [8], What-if [9], or InterpretML [10]. These methods help to better understand how the model works, and, thus, increase the user's trust in its functioning. They also facilitate error detection in the model's predictions, i.e., those that are inconsistent with the domain knowledge. These techniques are also gaining interest in medical applications and have been featured in well-known journals. The authors in [11] demonstrate how to use explanatory techniques in the analysis of deep neural network models for high-frequency electronic patient records. How explanatory models can complement ML models in predicting mortality for patients with kidney failures was described in [12]. Furthermore, complex ML models in the problem of early detection of circulatory failure in the Intensive Care Unit (ICU) were used in [13]. The authors in [14] present an explanation for models to predict hypoxia during surgery. However, medical data are not only tabular. Explainable models are used for image data analysis, X-rays, CTs, and ultrasounds alike. The authors in [15] more broadly discuss the list of methods used in medicine. The articles [16; 17] are focused on an explanatory analysis of radiological data, while the article [18] presents solutions for the detection of Acute Kidney Injuries (AKI) and Acute Lung Injuries (ALI). Moreover, lung diseases are also an object of scientific interest. There are a lot of publications regarding the application of artificial intelligence (AI) to lung diseases. Convolutional Neural Networks (CNNs) were used to differentiate between benign and malignant lung nodules [19]. The authors in [20] used machine learning models, such as Random Forests (RF) and Support Vector Machines, to find lung structure disease using exhaled aerosols. RF models were also used in the comprehensive study [21]. The authors propose a homemade e-nose system to detect lung cancer. Considerable effort and resources have been invested in proving that low-dose computed tomography (LDCT) is able to detect early lung cancer. The results of two randomized clinical trials (NLST and NELSON) have unequivocally demonstrated lung cancer mortality reduction in the intervention arm, i.e., individuals having scheduled a course of a baseline and annual LDCT, see [22; 23] for more details. From this time onward, even though there has been an ongoing debate regarding the details of multiple clinical research studies, we can no longer neglect the fact LDCT is capable of saving the lives of those at high risk of lung cancer.