{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:19:46Z","timestamp":1764249586520,"version":"3.46.0"},"reference-count":29,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T00:00:00Z","timestamp":1764201600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Artif. Intell."],"abstract":"<jats:p>The 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 (\u221255%), 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 for clinical decision-making. By leveraging biomarker relationships and machine learning algorithms, this study contributes to the development of personalized, non-invasive, and cost-effective diagnostic tools for respiratory diseases, ultimately improving patient outcomes and healthcare efficiency.<\/jats:p>","DOI":"10.3389\/frai.2025.1682774","type":"journal-article","created":{"date-parts":[[2025,11,27]],"date-time":"2025-11-27T13:15:56Z","timestamp":1764249356000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":0,"title":["Statistical and machine learning approaches for identifying biomarker associations in respiratory diseases in a population-specific region"],"prefix":"10.3389","volume":"8","author":[{"given":"Meshari","family":"Alazmi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Amer","family":"AlGhadhban","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abdulaziz","family":"Almalaq","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Kamaleldin B.","family":"Said","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yazeed","family":"Faden","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,11,27]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"110501","DOI":"10.1016\/j.compbiomed.2025.110501","article-title":"Advancing respiratory disease diagnosis: a deep learning and vision transformer-based approach with a novel X-ray dataset","volume":"194","author":"Alghadhban","year":"2025","journal-title":"Comput. 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