Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN

Scalable Clustering of Complex ECG Health Data: Big Data Clustering Analysis with UMAP and HDBSCAN

This study explores the potential of unsupervised machine learning algorithms to identify latent cardiac risk profiles by analyzing ECG-derived parameters from two general groups: clinically healthy individuals (Norm dataset, <i>n</i> = 14,863) and patients hospitalized with heart failur...

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Bibliographic Details
Main Authors: Vladislav Kaverinskiy, Illya Chaikovsky, Anton Mnevets, Tatiana Ryzhenko, Mykhailo Bocharov, Kyrylo Malakhov
Format: Article
Language:English
Published: MDPI AG 2025-06-01
Series:Computation
Subjects:
clustering analysis
data science
machine learning
bioinformatics
UMAP
HDBSCAN
Online Access:https://www.mdpi.com/2079-3197/13/6/144
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