## 1 Introduction  
The study of clinical and clinical studies has been conducted in the field of clinical studies (e.g., [1], [2], [3], [4], [5], [6], [7], [8], [9], [10], [11], [12], [13], [14], [15], [16], [17], [18], [19], [20], [21], [22], [2  
## 1 Introduction  
Syncope is a clinical condition characterized by a sudden and transient loss of consciousness caused by a temporary decrease in cerebral blood flow. Its onset is rapid, its duration is brief, and recovery is usually complete and spontaneous [1]. It is estimated that between 20% and 37% of patients with syncope experience recurrences within the first three years, a factor that has been associated with a higher risk of mortality and serious cardiovascular events [2; 3; 4]. Additionally, syncope carries a significant risk of trauma [5] and is associated with an all-cause mortality rate ranging from 4% to 6% [4]. It also negatively impacts quality of life and represents a considerable burden on healthcare systems [6]. The incidence of syncope shows two peaks: between the ages of 15 and 20, and around the age of 60, with the latter group exhibiting the highest morbidity and mortality [7]. From a pathophysiological perspective, syncope can be classified as reflex or neurally mediated, cardiogenic, or orthostatic [1]. Identifying the etiology is essential for guiding treatment and establishing a prognosis. However, the initial evaluation is often inconclusive, necessitating additional studies in many cases [8]. Despite these complementary evaluations, up to 48% of cases remain without an etiological diagnosis after 2.5 years of follow-up [9]. Furthermore, there are discrepancies among the recommendations of different scientific societies regarding the management of syncope [10]. The European Society of Cardiology, for example, suggests the early use of implantable loop recorders in patients with syncope of unclear cause (SUC) [1]. The clinical management of syncope remains complex, especially when the cause is uncertain, which complicates diagnosis and increases healthcare costs. Clinical decision rules have been developed to predict outcomes and stratify risk, but recent evidence suggests that most of these scales have limited predictive accuracy, restricting their utility in clinical practice [11]. In this context, artificial intelligence (AI) techniques, particularly machine learning (ML), have shown promising results in various areas of medicine [12; 13; 14; 15; 16; 17; 18]. However, their application in the field of syncope has been limited. Most previous studies on AI in syncope have used supervised ML methods for risk stratification, yielding only modest results, comparable to traditional approaches [19]. AI techniques have also been applied to improve diagnosis and risk prediction [20], but these studies have primarily focused on Holter and tilt-table data, which are not typically available in the early stages of diagnosis, when they could be most useful. Unsupervised ML techniques, particularly clustering methods, hold great potential in medicine, as they enable the identification of homogeneous patient groups within the clinical heterogeneity of a disease. This approach could facilitate better stratification and personalization of diagnostic and therapeutic strategies. In other conditions, such as COPD [21], clustering has proven useful for identifying clinical subtypes with prognostic and therapeutic implications. However, to date, few published studies have applied clustering methods in the context of SUC. The possibility of identifying syncope patient clusters based on clinical and demographic data--commonly available in the initial diagnostic phases--could allow for more precise analyses. This would facilitate the identification of patients at higher risk and promote more personalized and comprehensive care, potentially avoiding the need for invasive procedures such as implantable loop recorders.