[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125420-en":3,"doc-seo-125420-105":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},125420,1374391974585,"Genevieve","https://ap-avatar.wpscdn.com/davatar_276721f389ce27ea32af1340a28f341c",8,"Research & Report","Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region","Clinical blood biomarker–driven diagnostics provide a rapid, scalable alternative to imaging for early detection and prognosis of respiratory diseases. A retrospective cohort of 913 patients from a respiratory clinic in the Hail region assessed the diagnostic relevance of 15 blood biomarkers across COVID-19, pneumonia, asthma, and other complications. Statistical correlation analyses and decision tree machine learning models revealed biomarker interactions that differentiated conditions and achieved high predictive performance across disease categories.","TYPE Original Research PUBLISHED 27 November 2025 DOI 10.3389/frai.2025.1682774  \nOPEN ACCESS  \nEDITED BY  \nSunyoung Jang,  \nSUNY Upstate Medical University, United States  \nREVIEWED BY  \nAbedalmuhdi Almomany,  \nGulf University for Science and Technology, Kuwait  \nPrecious Idogun,  \nBeaumont Health, United States  \n*CORRESPONDENCE  \nMeshari Alazmi  \n [ms.alazmi@uoh.edu.sa](ms.alazmi@uoh.edu.sa)  \nRECEIVED 11 August 2025  \nREVISED 09 October 2025  \nACCEPTED 04 November 2025  \nPUBLISHED 27 November 2025  \nCITATION  \nAlazmi M, AlGhadhban A, Almalaq A,  \nSaid KB and Faden Y (2025) Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region.  \nFront. Artif. Intell. 8:1682774 .  \ndoi: 10.3389/frai.2025.1682774  \nCOPYRIGHT  \n© 2025 Alazmi, AlGhadhban, Almalaq,  \nSaid and Faden. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.  \nStatistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region  \nMeshari Alazmi 1,2*, Amer AlGhadhban 2,3, Abdulaziz Almalaq 2,3, Kamaleldin B. Said 2,4 and Yazeed Faden 5  \n1College of Computer Science and Engineering, University of Hail, Hail, Saudi Arabia, 2 Medical and Diagnostic Research Center, University of Hail, Hail, Saudi Arabia, 3College of Engineering, University of Hail, Hail, Saudi Arabia, 4 Department of Pathology, College of Medicine, University of Hail, Hail, Saudi Arabia, 5 Faculty of Computing and Information Technology, King Abdulaziz University, Rabigh, Saudi Arabia  \nThe growing interest in utilizing clinical blood biomarkers for non-invasive diagnostics has transformed the approach to early detection and prognosis of respiratory diseases. Biomarker-driven diagnostics offer cost-effective, rapid, and scalable alternatives to traditional imaging and clinical assessments. In this study, we conducted a retrospective analysis of 913 patients from a local respiratory clinic in Hail region, evaluating the diagnostic relevance of 15 blood biomarkers across four respiratory conditions: COVID-19, pneumonia, asthma, and other complications. Through data-driven analysis, statistical correlation assessments, and machine learning classification models (decision tree classifiers), we identified significant biomarker interactions that contributed to disease differentiation. Notably, CRP and HGB demonstrated a strong negative correlation (−55%), supporting the well-established role of systemic inflammation in anemia of chronic disease. Additionally, Ferritin and LDH exhibited a positive correlation (+50%), indicating metabolic stress and cellular injury in severe respiratory illnesses. Other significant correlations included Creatinine and ESR being negatively associated with RBC, while GGT and ALT were positively correlated (+49%) . Additionally, bilirubin and HGB were positively correlated (+49%), collectively reflecting systemic inflammatory and metabolic responses associated with respiratory pathology. The machine learning model demonstrated high predictive accuracy, with the following performance metrics: COVID-19: Precision (0 .94), Recall (0 .96), F1-score (0 .95) . Pneumonia: Precision (0 .97), Recall (0 .71), F1-score (0 . 85) . Asthma: Precision (1 .00), Recall (0 .95), F1-score (0 .97) . Other Complications: Precision (0 . 88), Recall (0 .90), F1-score (0 .90) . These findings validate the diagnostic potential of biomarker panels in respiratory disease classification, offering a novel approach to integrating statistical and computational modeling f","cbCaiuKOcP2EBwxf","https://ap.wps.com/l/cbCaiuKOcP2EBwxf","pdf",2250388,1,14,"English","en",105,"# 1 Introduction\n## Biomarkers for non-invasive diagnosis and prognosis\n## Blood-based markers in respiratory diseases","[{\"question\":\"What dataset and patient number were used in the study?\",\"answer\":\"The retrospective analysis used data from 913 patients treated in a local respiratory clinic in the Hail region.\"},{\"question\":\"Which machine learning approach was applied for classification?\",\"answer\":\"The study used decision tree classifiers to build predictive models for respiratory disease categorization.\"},{\"question\":\"What performance did the model achieve across the four respiratory conditions?\",\"answer\":\"The reported precision, recall, and F1-score were high overall, with Precision ranging from 0.88 to 1.00 and F1-scores around 0.85 to 0.97 depending on the condition.\"}]","Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region | 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