Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A
  Machine Learning Perspective
Maitham G. Yousif Fadhil G. Al-Amran Hector J. Castro
In this study, we leveraged machine learning techniques to identify risk
factors associated with post-COVID-19 mental health disorders. Our analysis,
based on data collected from 669 patients across various provinces in Iraq,
yielded valuable insights. We found that age, gender, and geographical region
of residence were significant demographic factors influencing the likelihood of
developing mental health disorders in post-COVID-19 patients. Additionally,
comorbidities and the severity of COVID-19 illness were important clinical
predictors. Psychosocial factors, such as social support, coping strategies,
and perceived stress levels, also played a substantial role. Our findings
emphasize the complex interplay of multiple factors in the development of
mental health disorders following COVID-19 recovery. Healthcare providers and
policymakers should consider these risk factors when designing targeted
interventions and support systems for individuals at risk. Machine
learning-based approaches can provide a valuable tool for predicting and
preventing adverse mental health outcomes in post-COVID-19 patients. Further
research and prospective studies are needed to validate these findings and
enhance our understanding of the long-term psychological impact of the COVID-19
pandemic. This study contributes to the growing body of knowledge regarding the
mental health consequences of the COVID-19 pandemic and underscores the
importance of a multidisciplinary approach to address the diverse needs of
individuals on the path to recovery. Keywords: COVID-19, mental health, risk
factors, machine learning, Iraq