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

Year: 2025

Type: article

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.

Source: Staffordshire Online Repository (Staffordshire University)

DOI: 10.48550/arxiv.2504.00007

Authors: Sadegh-Zadeh, Seyed Ali, Mamalo, Alireza Soleimani, Behnemoon, Mahsa, Ojarudi, Masoud, Gharebaghi, Naser, Pashaei, Mohammad Reza

Language: English

Topic: Long-Term Effects of COVID-19

Subfield: Neurology

Field: Medicine

Domain: Health Sciences

SDG: Good health and well-being

Open Access status: green

OpenAlex ID: W7128792285

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