  The COVID-19 pandemic has brought to light a concerning aspect of long-term
neurological complications in post-recovery patients. This study delved into
the investigation of such neurological sequelae in a cohort of 500
post-COVID-19 patients, encompassing individuals with varying illness severity.
The primary aim was to predict outcomes using a machine learning approach based
on diverse clinical data and neuroimaging parameters. The results revealed that
68% of the post-COVID-19 patients reported experiencing neurological symptoms,
with fatigue, headache, and anosmia being the most common manifestations.
Moreover, 22% of the patients exhibited more severe neurological complications,
including encephalopathy and stroke. The application of machine learning models
showed promising results in predicting long-term neurological outcomes.
Notably, the Random Forest model achieved an accuracy of 85%, sensitivity of
80%, and specificity of 90% in identifying patients at risk of developing
neurological sequelae. These findings underscore the importance of continuous
monitoring and follow-up care for post-COVID-19 patients, particularly in
relation to potential neurological complications. The integration of machine
learning-based outcome prediction offers a valuable tool for early intervention
and personalized treatment strategies, aiming to improve patient care and
clinical decision-making. In conclusion, this study sheds light on the
prevalence of long-term neurological complications in post-COVID-19 patients
and demonstrates the potential of machine learning in predicting outcomes,
thereby contributing to enhanced patient management and better health outcomes.
Further research and larger studies are warranted to validate and refine these
predictive models and to gain deeper insights into the underlying mechanisms of
post-COVID-19 neurological sequelae.

Machine Learning;Biomolecules;Neurons and Cognition