Clinical Characterization of Patients with Syncope of Unclear Cause Using Unsupervised Machine-Learning Tools: A Pilot Study

Syncope of unclear cause (SUC) presents a significant diagnostic challenge, with a considerable proportion of patients remaining without a definitive diagnosis despite comprehensive clinical evaluation. This study aims to explore the potential of unsupervised machine learning (ML), specifically clustering algorithms, to identify clinically meaningful subgroups within a cohort of 123 patients with SUC. Patients were prospectively recruited from the cardiology, neurology, and emergency departments, and clustering was performed using the k-prototypes algorithm, which is suitable for mixed-type data. The number of clusters was determined through cost function analysis and silhouette index, and visual validation was performed using UMAP. Five distinct patient clusters were identified, each exhibiting unique profiles in terms of age, comorbidities, and symptomatology. After clustering, nocturnal cardiorespiratory polygraphy and heart rate variability (HRV) parameters were analyzed across groups to uncover potential physiological differences. The results suggest distinct autonomic and respiratory patterns in specific clusters, pointing toward possible links among sympathetic dysregulation, sleep-related disturbances, and syncope. While the sample size imposes limitations on generalizability, this pilot study demonstrates the feasibility of applying unsupervised ML to complex clinical syndromes. The integration of clinical, autonomic, and sleep-related data may provide a foundation for future, larger-scale studies aiming to improve diagnostic precision and guide personalized management strategies in patients with SUC.