Predicting Long-term Renal Impairment in Post-COVID-19 Patients with Machine Learning Algorithms
Maitham G. Yousif, Hector J. Castro, John Martin, Hayder A. Albaqer,, Fadhil G. Al-Amran, Habeeb W. Shubber, Salman Rawaf

TL;DR
This study uses machine learning algorithms on data from 821 post-COVID-19 patients in Iraq to predict long-term renal impairment, aiming to improve early intervention and patient outcomes.
Contribution
It introduces predictive models for renal impairment risk in post-COVID-19 patients using comprehensive machine learning techniques and a diverse patient dataset.
Findings
Machine learning models achieved high predictive accuracy.
Identified key risk factors for renal impairment.
Potential for early intervention in clinical practice.
Abstract
The COVID-19 pandemic has had far-reaching implications for global public health. As we continue to grapple with its consequences, it becomes increasingly clear that post-COVID-19 complications are a significant concern. Among these complications, renal impairment has garnered particular attention due to its potential long-term health impacts. This study, conducted with a cohort of 821 post-COVID-19 patients from diverse regions of Iraq across the years 2021, 2022, and 2023, endeavors to predict the risk of long-term renal impairment using advanced machine learning algorithms. Our findings have the potential to revolutionize post-COVID-19 patient care by enabling early identification and intervention for those at risk of renal impairment, ultimately improving clinical outcomes. This research encompasses comprehensive data collection and preprocessing, feature selection, and the…
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Taxonomy
TopicsCOVID-19 and healthcare impacts
