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
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 development of
predictive models using various machine learning algorithms. The study's
objectives are to assess the incidence of long-term renal impairment in
post-COVID-19 patients, identify associated risk factors, create predictive
models, and evaluate their accuracy. We anticipate that our machine learning
models, drawing from a rich dataset, will provide valuable insights into the
risk of renal impairment, ultimately enhancing patient care and quality of
life. In conclusion, the research presented herein offers a critical
contribution to the field of post-COVID-19 care. By harnessing the power of
machine learning, we aim to predict long-term renal impairment risk accurately.
These predictions have the potential to inform healthcare professionals,
enabling them to take proactive measures and provide targeted interventions for
post-COVID-19 patients at risk of renal complications, thus minimizing the
impact of this serious health concern.