## 2 Materials and Methods
### Conceptual Design of the Study  
Figure 1 presents a conceptual diagram outlining the study methodology. In Stage 1, the clustering of the cohort was conducted. Prior to this, a thorough data preprocessing was performed. The distribution of numerical variables was assessed using the skewness coefficient, calculated according to the moment-based Fisher-Pearson method [26]. Several variables with moderate or high skewness were identified (e.g., total number of syncopal episodes, SDNN, SDANN), which could distort the distance calculations used in clustering techniques. Since skewed distributions may bias clustering results, the Box-Cox transformation [27] was applied to those numerical variables with skewness greater than 0.5, after adding one unit to each value to ensure positivity. Subsequently, all numerical variables were rescaled using min-max normalization to bring them into a common range [0, 1]. This sequence of transformations helped reduce skewness and prevent certain numerical variables from dominating the distance calculations. Categorical variables were included as categorical inputs, allowing the algorithm to handle them using specific dissimilarity measures based on category matching, without requiring additional transformation. To avoid biases introduced by artificial imputations in an unsupervised approach, patients with missing values in any of the selected variables were excluded from the analysis. As a result, out of the 179 patients initially available in the cohort, a total of 123 were ultimately included in the clustering analysis. Since the dataset includes both continuous and categorical variables [28; 29], the k-prototypes [30] algorithm was selected, as it is specifically suited for mixed-type data. This algorithm combines Euclidean distance for numerical variables with a dissimilarity measure based on mode matching for categorical variables, thus allowing a unified computation of the total distance. To determine the optimal number of clusters, the elbow method was used--a graphical heuristic approach to identify the optimal number of clusters [31]. Clustering was performed using the variables listed in Table 1. Following the analysis, five clusters were identified. To explore different clustering solutions, values of k ranging from 2 to 10 were evaluated. In each case, the algorithm was initialized using the Cao method [32], and multiple random restarts were performed to enhance the stability of the solution. As previously mentioned, the selection of the optimal number of clusters was based on the elbow method [31] applied to the model's cost function and complemented by the calculation of the silhouette index [33] using the Gower distance matrix [34]. This metric allowed for a quantitative assessment of the cohesion and separation among the generated groups. The five-cluster solution was ultimately selected as it offered a suitable balance among internal consistency, clinical relevance, and interpretability. As an additional visual validation, the UMAP (Uniform Manifold Approximation and Projection for Dimension Reduction) technique [35] was applied, also using the Gower distance matrix, to graphically represent the distribution of patients and the separation among clusters in a two-dimensional space. This unsupervised technique preserves both the local and global structure of the original dataset. For its implementation, the number of neighbors was set to 30 and the minimum distance between points to 0.3--values commonly used in exploratory visualizations. This projection was employed exclusively for illustrative purposes, providing visual support for the cluster structure identified through k-prototypes. Once the clusters were defined, Stage 2 involved a statistical analysis to assess differences among them. At this stage, both the variables shown in Table 1 and those in Table 2 were included. For continuous variables, the Mann-Whitney U test [36] was used, while  
Figure 1: Study methodology. The process consists of two stages. Before Stage 1, the data is preprocessed.