## 2 Materials and Methods
### Dataset Description and Pre-Processing  
Data acquisition was carried out as part of a three-year project conducted by the Norwegian Centre for E-health Research, UiT The Arctic University of Norway, and Healthcom [24]. This study was approved by the Data Protection Section of the University Hospital of North Norway, and all participants signed the corresponding informed consent. The dataset was obtained through a survey questionnaire made to a population from Norway using a smartphone-based solution over a 2-year period [24]. The responses to the survey were anonymized after their submission. The aim was to track modifiable risk factors for four non-communicable diseases: CVD, diabetes, cancer, and chronic respiratory illness. However, during the data acquisition campaign, only individuals affected by CVD (\(n=465\)), cancer (\(n=72\)), and both CVD and cancer (\(n=46\)) diseases were collected. Additionally, 1578 healthy individuals filled out the questionnaire. Due to the low number of individuals with cancer and both diseases, in this work, we only focused on CVD and healthy subjects. In summary, a total of 2043 individuals were included in the subsequent analysis. The dataset consists of 26 features and was organized into five different groups: _background_, _substance use_, _PA_, _dietary intake_, and _income_. Questions related to alcohol followed a disorders identification test [37], and information about PA was based on the International Physical Activity Questionnaire [38]. A summary of the categories associated with each feature is presented in Table 1.  
Since all features in our study were categorical, for an exploratory analysis, we followed a one-hot encoding strategy to encode each category in a specific variable by assigning the value of '0'/'1'. To better characterize and visually analyze the prevalence of certain categories compared to others, we built a representation named profile [39], showing the most prevalent risk factors for a specific disease. The profile is a one-dimensional visual representation, where the x-axis shows all the categories and the y-axis represents the frequency ratio of occurrence of each of the categories for a specific group of individuals. Figure 1 shows the profiles for both healthy and CVD subjects related to the five main groups of variables previously presented. Prior to building profiles and aiming to deal with missing data, we performed the following steps. First, we identified the number of missing values (represented by 'NAN' identifier) for each feature, which indicates that the answer is not available. Since the proportion of 'NAN' was low compared to the proportions of the rest categories, we adopted a simple imputation approach by replacing 'NAN' with a new category named 'NA' (not available answer for a question). As shown in Figure 1, the values of 'NA' in each feature are lower compared to other categories, which could suggest that the imputation approach used to compensate for missing values will have a slight impact on the data distribution of features. In Figure 1a, focusing on the features with the highest frequency of appearance (age), we observe that the population aged in the range between 60 and 69 years predominates for CVD subjects. By analyzing sex, we observe that there is a higher percentage of male subjects with CVD, while in the healthy group the frequency of occurrence is higher in women. Regarding BMI, there is a higher frequency of healthy people with a healthy weight (HW) with respect to the CVD group. Finally, note that presence of high cholesterol is clearly linked to the group of CVD subjects. In Figure 1b, related to the consumption of harmful substances, we observe that a high percentage of individuals are non-smokers, this percentage being higher among the healthy subjects. In general, individuals of both groups report alcohol consumption, though it is usually one or two units per week. Figure 1c shows how PA is performed.