[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-118871-en":3,"doc-seo-118871-105":30,"detail-sidebar-cat-0-en-105":92},{"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},118871,8796095360427,"Lucas Martin","https://ap-avatar.wpscdn.com/davatar_994ba38a5ba835b3df7d355c54d3ed8d",8,"Research & Report","Predicting Cardiovascular Complications in Post-COVID-19 Patients Using Data-Driven Machine Learning Models","The COVID-19 pandemic has triggered widespread health challenges, with post-COVID-19 cardiovascular complications drawing growing clinical attention. This study predicts cardiovascular complications during recovery by applying data-driven machine learning models to a cohort of 352 post-COVID-19 patients across Iraq. Demographic variables, comorbidities, laboratory measures, and imaging findings were compiled to build and evaluate predictive models using training and testing splits. Results highlight significant associations between selected comorbidities and subsequent cardiovascular complications, producing strong performance across accuracy, sensitivity, and specificity. Early risk detection supports timely intervention and improved patient outcomes.","Predicting Cardiovascular Complications in Post-COVID-19 Patients Using DataDriven Machine Learning Models  \nMaitham G. Yousif*1 , Hector J. Castro2  \n1 Biology Department, College of Science, University of Al-Qadisiyah, Iraq, Visiting Professor in Liverpool John Moors University, Liverpool, United Kingdom  \n2Specialist in Internal Medicine-Pulmonary Disease in New York, USA Received 1/9/2022, Accepted 2/4/2023, Published 14/8/2023 .  \n This work is licensed under a Creative Commons Attribution 4.0 International License.  \nAbstract  \nThe COVID-19 pandemic has led to widespread health challenges globally. Among these challenges, the emergence of post-COVID-19 complications, particularly cardiovascular complications, has garnered significant attention. This study addresses the pressing issue of predicting cardiovascular complications in individuals recovering from COVID-19 by employing data-driven machine learning models. A comprehensive analysis was conducted, encompassing a cohort of 352 post-COVID-19 patients from diverse regions of Iraq. Pertinent clinical data, comprising demographic information, comorbidities, laboratory findings, and imaging results, were meticulously collected. Machine learning algorithms, including [Specify the algorithms employed], were harnessed to construct predictive models. The dataset was stratified into training and testing subsets to rigorously assess the model performance. The study's outcomes illuminated several critical insights, such as the identification of substantial associations between specific comorbidities and the occurrence of postCOVID-19 cardiovascular complications. The predictive models achieved commendable accuracy rates, sensitivity, specificity, and other relevant performance metrics, thus demonstrating their efficacy in recognizing individuals at heightened risk of developing such complications. This early detection capability holds promise for facilitating timely interventions, ultimately resulting in improved patient outcomes. In conclusion, this investigation underscores the potential of data-driven machine learning models as invaluable tools for predicting cardiovascular complications in individuals convalescing from COVID-19. The findings accentuate the necessity for vigilant monitoring of patients, particularly those with identifiable risk factors. Furthermore, this study advocates for continued research efforts and validation studies to refine these models, enhancing their accuracy and generalizability in diverse clinical settings.  \nKeywords: COVID-19, post-COVID-19 complications, cardiovascular complications, machine learning, predictive modeling, 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 has ushered in an era of unparalleled challenges and critical questions regarding the virus's multifaceted impacts on human health and well-being (1-8) . Among the myriad aspects that researchers worldwide have been diligently investigating are the intricate relationships between COVID-19 and various aspects of human physiology and pathology (9- 20) . This pursuit is critical, as it contributes significantly to our understanding of the virus's pathogenesis and informs strategies for effective management and mitigation of its effects. Consumer Behavior during the pandemic has been a subject of keen interest, as observed in research by Murugan et al.(2022) [21] . Their work focuses on predicting consumer behavior during pandemic conditions using sentiment analytics. Understanding how consumers respond to such crises is critical for businesses and policymakers. Another area of research has been the detection of insincere questions on platforms like Quora, as explored by Chakraborty et al. (2022) [22] . Their attention-based model for classifying insincere questions can help maintain the quality of online discussions during these times. 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