## 1 Introduction
Recent studies have shown that exposure to heavy metals increases risks of diabetes and hypertension [21; 22]. Some essential (Co, Cu, Cr, Ni, Se) and toxic metals (As, Cd, Pb, and Hg) for the human body may also increase the risk of CVD through endocrine disruption [23]. Few epidemiologic studies in the US have highlighted a low and moderate association between arsenic exposure contained in drinking water and cardiovascular risks [24; 25]. Furthermore, [PERSON] et al. [26] reported that carotid atherosclerosis is linked with ingested inorganic arsenic. The association between blood cadmium and blood pressure is mentioned by [PERSON] and [PERSON] [27] while [PERSON] et al. [28] found that an accumulation of mercury in the body is linked with an excess risk of acute myocardial infarction, just to name a few works. In general, in the last two decades evidence about the role of exposure to environmental heavy metals in CVD risk has rapidly increased [29]; however, the true mechanism by which heavy metals affect cardiovascular risk factors still remains quite unknown and difficult to identify [30]. Due to the complex physiology of CVD, patients differ for some pathological aspects of disease such as: the time of onset, dynamics, and outcomes. This disease-related complexity makes the relevance of environmental factors difficult to quantify and study despite a growing understanding of the genetic, protein and molecular mechanisms involved in CVD [30]. Accordingly, in this work we investigate the association between single nucleotide polymorphisms (SNPs) and pathological conditions related to environmental heavy metals focusing on cardiovascular diseases. Human scalp hair analysis (HSHA) is an alternative to blood and urine testing used to monitor the environmental and occupational pollution [31; 32; 33]. It is highly reliable and accurate even when heavy metal concentrations are very low. It is accepted by the US Food and Drugs Administration and by several International Agencies and organizations such as the Environmental Protection Agency (EPA) and World Health Organization (WHO). In this context, the development of accurate diagnostic decision support systems, based on HSHA tests, for the early detection of cardiovascular diseases to monitor patient conditions and assess the severity of symptoms is of paramount importance. As far as we know, we are the first to use a machine learning approach to correlate HSHA data with polymorphisms that specifically affect the coagulation cascade and the integrity of the vascular endothelium. In this context, machine learning methods can also help to clarify the connection between environmental factors, in particular heavy metal exposure and CVD risks, since these computational techniques do not require any a priori assumptions on the model and allow to highlight even very complex relationships. For this purpose, we enrolled a cohort of 90 patients affected by CVD at the DD Clinic foundation ([[https://www.ddclinicfoundation.eu/](https://www.ddclinicfoundation.eu/)]([https://www.ddclinicfoundation.eu/](https://www.ddclinicfoundation.eu/)), accessed on 31 August 2021) of Caserta, Italy. The DD Clinic foundation finds its mission within the Integrated Personalized Medicine domain, specifically, aiming at the elaboration of cellular detoxification protocols from heavy metals with a particular attention to functional foods and nutraceutical supports to slow down or block the specific genetic predispositions of chronic degenerative diseases. We examined HSHA tests of patients involved and we grouped them into three clinical classes associated with three distinct levels of clinical risk. We used a machine learning approach, to evaluate which are the most important heavy metals associated to the aforementioned three clinical groups and their predictive power.