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.
@article{arxiv.2504.00007,
title = {Clustering Analysis of Long-term Cardiovascular Complications in COVID-19 Patients},
author = {Seyed Ali Sadegh-Zadeh and Alireza Soleimani Mamalo and Mahsa Behnemoon and Masoud Ojarudi and Naser Gharebaghi and Mohammad Reza Pashaei},
journal= {arXiv preprint arXiv:2504.00007},
year = {2025}
}