# Explainable Machine Learning for Lung Cancer Screening Models
## 5 Discussion and Conclusions  
Lung cancer risk models are important, as they facilitate enrolling at-risk population in the screening process, thereby indirectly decreasing the mortality of one of the most common and aggressive cancers. To this end, the complex statistical risk models are increasingly used. Moreover, there are studies that compare the models in terms of the accuracy of the specific datasets, for example, ref. [31]. In our study, we fill the gap related to the interpretability of these risk models. In recent years, explainable machine learning techniques have become increasingly appreciated and are currently being widely used in different fields. The XAI methods have already been applied to lung cancer problems in [40,41]. This study presents how models that are widely recognized could be equipped with explainable machine learning techniques.  
We demonstrated the approach to applying methods of explainable machine learning to three risk models. XAI methodology provides insight into the question regarding which patients' variables are the most important. Additionally, we presented global solutions that help to understand the relationships between a particular variable and a model's response. We believe that in the field of medicine, local explanations are even more important. Such solutions help to understand how a particular variable affects the final prognosis for a given patient. Moreover, local methods could help ascertain what can be done to change model prediction. However, local explanations can be unstable, mainly because of the underlying model being unstable. In our study, the models seems to be stable as the local explanations show smooth dependencies.  
The global methods applied to screening models on the Polish database show that different models treat the dependency between some of the variables and the prediction in distinct ways. Based on the PDP method, we observed that quit smoking time, years smoked, and age have different dependencies among the models. Moreover, the Bach model indicates that years smoked are much more important than other variables. For other models, this variable's influence does not differ in such a significant way. On the other hand, the local explaining methodology provides a wealth of information on prediction for individual patients. The local explanations presented in our study vary from each other because they are applied to three different models. Therefore, the explanation methods help us to notice the differences between those models. Overall, the local point of view is a step toward personalized medicine.  
Conceptualization, K.K., M.A. and P.B.; Data curation, K.K., T.O., M.A. and P.B.; Formal analysis, K.K. and P.B.; Funding acquisition, P.B.; Investigation, K.K., M.A. and P.B.; Methodology, K.K. and P.B.; Project administration, P.B.; Resources, K.K., T.O., M.A. and P.B.; Software, K.K.; Supervision, M.A. and P.B.; Validation, K.K.; Visualization, K.K.; Writing--original draft, K.K. and P.B.; Writing--review and editing, K.K., M.A. and P.B. All authors have read and agreed to the published version of the manuscript.  
The article was financially supported by NCN Sonata Bis-9 grant 2019/34/E/ST6/00052 and INFOSTRATEG-I/0022/2021-00.  
Not applicable.  
The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results.