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

翻译：本研究利用机器学习技术识别与COVID-19后精神健康障碍相关的风险因素。基于对伊拉克各省669名患者的数据分析，我们获得了有价值的见解。研究发现，年龄、性别和居住地理区域是影响COVID-19后患者发生精神健康障碍可能性的关键人口学因素。此外，合并症和COVID-19疾病的严重程度是重要的临床预测指标。社会支持、应对策略和感知压力水平等社会心理因素也发挥了重要作用。我们的发现强调了COVID-19康复后多种因素在精神健康障碍发展中的复杂交互作用。医疗提供者和政策制定者在设计针对高风险个体的干预和支持系统时，应考虑这些风险因素。基于机器学习的方法可为预测和预防COVID-19后患者的不良精神健康结果提供有价值的工具。需要进一步研究和前瞻性研究来验证这些发现，并增进我们对COVID-19大流行长期心理影响的理解。本研究为关于COVID-19大流行精神健康后果的日益增长的知识体系做出了贡献，并强调了采用多学科方法以满足康复道路上个体多样化需求的重要性。关键词：COVID-19，精神健康，风险因素，机器学习，伊拉克