## 4 Discussion  
The presence of heavy metals in the environment where humans live, represents a factor of great importance in the onset of degenerative pathologies (cardiovascular and neurodegenerative diseases, neoplasia, etc...) as clearly demonstrated by a lot of scientific works. For example, [PERSON] et al. by means of scalp hair analysis implemented in the industrial city of Taranto reported an increase of lung cancer risk among residents due to the high concentration of heavy metals [48]. In the present work, we focused on the relation between heavy metals and cardiovascular diseases. We applied a learning methodology based on Artificial Intelligence algorithm to analyze a private database containing 27 heavy metal concentrations and 25 polymorphisms linked to the cardiovascular diseases of 90 patients enrolled by DD Clinic foundation of Caserta in Italy. Through a machine learning procedure based on three state of the art algorithms, we classified patients with cardiovascular diseases in three classes of risk. Furthermore, we adopted a sampling strategy based on SMOTE method to allow the best operational conditions for each classifier. The best-performing method was RF in terms of all used classification metrics as shown in are shown in Figure 3 and in Table 3. We observed that, for all pairwise comparisons, the agreement between the classification models is not very high (42-46%). We investigated, by means of the Boruta wrapper method, which features were best at characterizing the differences between the three levels of cardiovascular risk. We observed that a small amount of features (25% of the initial dataset, as reported in Table 4) was sufficient for an accurate classification. Furthermore, we adopted different cross validation frameworks to maximize the performance of each RF classifier. We obtained good classification performances (0.78 of AUC), considering a three classes problem, when we used RF fed by the Boruta features in LOO cross validation, as shown in Figure 6 and in Table 5. The discriminating power of these selected features is higher than that of the entire set of features; but also of the heavy metal concentrations and of the polymorphisms taken separately, as displayed in Figure 7. This means that Boruta has efficiently eliminated noise and redundant feature. The use of Boruta is also important to assess the importance of different predictors and thus provide clinical interpretable findings. Furthermore, the results summarized in Figure 7 show how the implemented wrapper method strategy improves the classification performance. Although the size of input data could appear small for a machine learning approach, our findings show RF, with SMOTE, Boruta and cross validation, is a robust framework for CVD classification. However the application of an oversampling technique does not solve completely the data imbalance issue. In fact, the minority class HR presents ever the worst performances. Our results show that the starting hypothesis that heavy metal concentrations and SNPs can accurately model the severity of CVD was correct. However the selected features displayed in Table 4 testify that the association between environmental factors and CVD happens by means of complex biological and genetic pathways. The presence of these pathways was highlighted thanks to a machine learning approach and therefore without a priori assumptions about the relationship between the input features and the severity of CVD. These pathways should be studied further to understand how heavy metals affect cardiovascular disease. To confirm the goodness of our findings in previous works, some of the selected features had already been highlighted as linked to cardiovascular diseases. For example, [PERSON] et al., by analyzing 37 unique studies comprising 348259 participants, concluded that exposure to lead (as well as to arsenic, cadmium, and copper) is associated with an increased risk of cardiovascular disease and coronary heart disease [49]. [PERSON] et al.