Publications by authors named "Luka Biedebach"
Article Synopsis
- Obstructive sleep apnea (OSA) is a diverse sleep disorder, and researchers have previously analyzed its different phenotypes using various clustering methods.
- This study tested four clustering techniques (Agglomerative Hierarchical Clustering, K-means, Fuzzy C-means, and Gaussian Mixture Model) on 865 patients to see how method selection influences the classification of OSA clusters and their physiological differences.
- Results showed that two clusters were consistently distinct across all methods, while three had overlapping features; K-means performed best overall, and Fuzzy C-means excelled in managing overlapping clusters, underscoring the significance of clustering method choice in OSA phenotyping.
