# Explainable Machine Learning for Lung Cancer Screening Models
## 4 Results
### Individual-Level Explanations  
Individual-level explanations help to understand model behavior in the context of a single prediction/single patient. This perspective is much more interesting for a patient who is less interested in average behavior and most often is interested in his or her own situation. Such a local glimpse on a patient prediction can be viewed as personalized medicine. The two key questions are (1) which variables are the most important for this particular patient and (2) how the risk prediction for this patient changes with the variable. In the current study, we focused on an observation chosen from the data. We chose a patient with the greatest disparities among the models' predictions. The analysis was conducted for a person with a high lung cancer risk, according to the PLCO\({}_{m2012}\) model (over 10%), and a low risk, according to the BACH model (below 2%, which is the screening threshold). The LCRAT model indicates a risk only slightly above the threshold. Our goal was to present the main local differences between the models and point out their causes. Figure 4 shows which variables are the most important for the selected patient. Two methods that present the contributions of local variables to the final prediction are demonstrated: (A) break down and (B) Shapley Additive Explanations (Shap). The first method presents nonadditive explanations, whereas the second one focuses only on additive expla  
Figure 3: Partial Dependence Profiles illustrate the relationship between a variable and an average model prediction. On the \(x\)-axis, the range of available values of the analyzed variable is shown, while on the \(y\)-axis, the values of the mean prediction are shown. nations. The comparison of these two methodologies shows that the results of the break down [39] and Shapley attributions [5] are very similar. The only difference concerns the contribution of age equal to 90 for the BACH model. The break down plot indicates its contribution as positive, whereas Shapley indicates it as a negative contribution. In addition, comparison of the models indicates more differences. Regarding break down plots, the LCRAT and PLCO\({}_{m2012}\) models indicate that the patient has a high risk of lung cancer, whereas the BACH model assigns a very low prediction (lower than the 2% threshold). The most important difference between the models seems to be the contribution of quit smoking time. Only the PLCO\({}_{m2012}\) model suggests that being a current smoker (quit smoking value equal to zero) increases the prediction in such a significant manner. The second difference among the models is the impact of age. Age equal to 90 increases the prediction for the PLCO\({}_{m2012}\) and LCRAT models, whereas, according to the Bach model, age has a contribution close to zero. In fact, according to the PLCO\({}_{m2012}\) and LCRAT models, age is the most important variable for that patient. Cigarettes per day equal to 20 have opposite effects across the models. The LCRAT model recognizes it as a negative contributed variable, whereas the PLCO\({}_{m2012}\) and BACH models suggest its positive effect. It is worth noting that the BMI measure has quite a significant influence on prognosis for the LCRAT and PLCO\({}_{m2012}\) models, while it is not included in the BACH model. Figure 5 illustrates how the risk prediction for the selected patient changes with the variable. Ceteris Paribus plots present the change of prediction within the change of the variable for the selected patient. The profiles indicate major differences between the PLCO\({}_{m2012}\) model and the two other models. Figure 4: The first column (**A**) presents break down plots for additive attributions. The second column (**B**) presents Shapley additive explanations. (**A**) The deep purple bar indicates the final prediction for a chosen patient. The plot also shows the contributions of each variable to the final prognosis. Green bars indicate positive contributions, meaning that the value of a certain variable implies an increase in prediction.