[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-120828-en":3,"doc-seo-120828-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},120828,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Identifying Risk Factors for Post-COVID-19 Mental Health Disorders - A Machine Learning Perspective","This study leverages machine learning to identify risk factors linked to post-COVID-19 mental health disorders using clinical and demographic data from 669 patients across multiple provinces in Iraq. Analyses highlight age, gender, and residential region as key demographic predictors, while comorbidities and COVID-19 illness severity provide important clinical signals. Psychosocial variables—including social support, coping strategies, and perceived stress—also substantially shape risk. Results support targeted prediction and prevention strategies for mental health outcomes after recovery, with findings requiring further validation through prospective research.","Identifying Risk Factors for Post-COVID-19 Mental Health Disorders: A Machine Learning Perspective  \nMaitham G. Yousif*1 , Fadhil G. Al-Amran2, Hector J. Castro3  \n1 Biology Department, College of Science, University of Al-Qadisiyah, Iraq, Visiting Professor in Liverpool John Moors University, Liverpool, United Kingdom  \n2Cardiovascular Department, College of Medicine, Kufa University, Iraq 3Specialist in Internal Medicine-Pulmonary Disease in New York, USA Received 3/10/2022, Accepted 2/2/2023, Published 1/8/2023  \n This work is licensed under a Creative Commons Attribution 4.0 International License.  \nAbstract  \nIn this study, we leveraged machine learning techniques to identify risk factors associated with postCOVID-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.  \nKeywords: COVID-19, mental health, risk factors, machine learning, Iraq  \n*Corresponding author: Maithm Ghaly Yousif [matham.yousif@qu.edu.iq](matham.yousif@qu.edu.iq)  [m.g.alamran@ljmu.ac.uk](m.g.alamran@ljmu.ac.uk)  \nIntroduction  \nThe COVID-19 pandemic, caused by the novel coronavirus SARS-CoV-2, has not only posed a significant threat to global public health but has also brought to light various indirect consequences affecting individuals' mental wellbeing[1-5] . As healthcare systems around the world grapple with the immediate challenges of treating COVID-19 patients, it has become increasingly evident that there is a pressing need to understand and address the potential long-term mental health repercussions of this global crisis. Numerous studies have reported a spectrum of mental health issues emerging in the wake of COVID-19 recovery, including anxiety, depression, post-traumatic stress disorder (PTSD), and other neuropsychiatric disorders[4-6] . These conditions, often collectively referred to as post-COVID-19 mental health disorders, can be debilitating and require comprehensive evaluation, risk assessment, and timely intervention. To effectively mitigate these mental health challenges, it is imperative to identify the risk factors contributing to their development. Machine learning, with its capacity to analyze vast datasets and extract  \nintricate patterns, presents an invaluable tool for this purpose[7-9] . By leveraging data-driven insights, we can gain a deeper understanding of the variables and circumstances that predispose individuals to post-COVID-19 mental health disorders. In this study, we utilize a machine learning perspective to identify key risk factors associated with the onset of mental health disorders in individuals recovering from COVID- 19. Our dataset comprises medical","cbCaiu0w5NCTC2OM","https://ap.wps.com/l/cbCaiu0w5NCTC2OM","pdf",342047,1,12,"English","en",105,"# Abstract\n# Introduction\n# Methodology\n## Data Collection\n## Study Design\n## Statistical Analysis\n## Machine Learning Analysis","[{\"question\":\"Which demographic factors were associated with post-COVID-19 mental health disorders?\",\"answer\":\"Age, gender, and the geographical region of residence significantly influenced the likelihood of developing mental health disorders.\"},{\"question\":\"What clinical variables predicted post-COVID-19 mental health disorders?\",\"answer\":\"Comorbidities and the severity of COVID-19 illness were important clinical predictors of mental health outcomes.\"},{\"question\":\"How did the study model risk using machine learning?\",\"answer\":\"The dataset was preprocessed to handle missing data and encode categorical variables, then a machine learning pipeline was built with training and testing data splits.\"}]","Identifying Risk Factors for Post-COVID-19 Mental Health Disorders - 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