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The COVID-19 pandemic has brought to light a concerning aspect of long-termneurological complications in post-recovery patients. This study delved intothe investigation of such neurological sequelae in a cohort of 500post-COVID-19 patients, encompassing individuals with varying illness severity.The primary aim was to predict outcomes using a machine learning approach basedon diverse clinical data and neuroimaging parameters. The results revealed that68% 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 modelsshowed promising results in predicting long-term neurological outcomes.Notably, the Random Forest model achieved an accuracy of 85%, sensitivity of80%, and specificity of 90% in identifying patients at risk of developingneurological sequelae. These findings underscore the importance of continuousmonitoring and follow-up care for post-COVID-19 patients, particularly inrelation to potential neurological complications. The integration of machinelearning-based outcome prediction offers a valuable tool for early interventionand personalized treatment strategies, aiming to improve patient care andclinical decision-making. In conclusion, this study sheds light on theprevalence of long-term neurological complications in post-COVID-19 patientsand 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 thesepredictive models and to gain deeper insights into the underlying mechanisms ofpost-COVID-19 neurological sequelae.

Long-term Neurological Sequelae in Post-COVID-19 Patients: A Machine Learning Approach to Predict Outcomes