Unsupervised Clustering of Patients Undergoing Thoracoscopic Ablation Identifies Relevant Phenotypes for Advanced Atrial Fibrillation

Unsupervised Clustering of Patients Undergoing Thoracoscopic Ablation Identifies Relevant Phenotypes for Advanced Atrial Fibrillation

<b>Background/Objectives</b>: The rate of recurrence after ablation for atrial fibrillation (AF) is considerable. Risk stratification for AF recurrence after ablation remains incompletely developed. Unsupervised clustering is a machine learning technique which might provide valuable insi...

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Bibliographic Details
Main Authors: Ilse Meijer, Marc M. Terpstra, Oscar Camara, Henk A. Marquering, Nerea Arrarte Terreros, Joris R. de Groot
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Diagnostics
Subjects:
unsupervised machine learning
principal component analysis
K-Means
thoracoscopic ablation
atrial fibrillation
phenotyping
Online Access:https://www.mdpi.com/2075-4418/15/10/1269
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