Clustering Analysis of Long-term Cardiovascular Complications in COVID-19 Patients

2025/03/23 by Sadegh-Zadeh, Seyed Ali, Mamalo, Alireza Soleimani, Behnemoon, Mahsa +3
#Applications (stat.AP) #FOS: Biological sciences #FOS: Computer and information sciences #Quantitative Methods (q-bio.QM)

paper · doi:10.48550/arxiv.2504.00007

Abstract

This study investigates long-term cardiovascular complications in COVID-19 patients using advanced clustering techniques. The objective was to analyse ECG parameters, demographic data, comorbidities, and hospitalization details to identify patterns in cardiovascular health outcomes. We applied K-means clustering and identified three distinct clusters: Cluster 0 with moderate heart rate variability and ICU admissions, Cluster 1 with lower heart rate variability and ICU admissions, and Cluster 2 with higher heart rate variability and ICU admissions, indicating higher risk profiles.

Related

• Identifying Subgroups of ICU Patients Using End-to-End Multivariate Time-Series Clustering Algorithm Based on Real-World Vital Signs Data
• Unsupervised clustering of single-lead electrocardiograms associates with prevalent and incident heart failure in coronary artery disease
• Cluster-weighted modeling of lifetime hierarchical data for profiling COVID-19 heart failure patients
• Uncover mortality patterns and hospital effects in COVID-19 heart failure patients: a novel Multilevel logistic cluster-weighted modeling approach
• Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models