{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,17]],"date-time":"2026-03-17T12:06:03Z","timestamp":1773749163128,"version":"3.50.1"},"reference-count":44,"publisher":"MDPI AG","issue":"3","license":[{"start":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T00:00:00Z","timestamp":1772150400000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Information"],"abstract":"<jats:p>Infectious diseases are contributing to a major public health challenge worldwide, affecting individuals across all age groups and regions. An infectious disease is a pathological condition caused by harmful microorganisms. These are bacteria, viruses, fungi, or parasites that enter the body, multiply, and disturb normal physiological functions, leading to clinical manifestations. At present, the detection of infectious disease is mainly based on vital signs and a limited set of biomarkers. This limited approach fails to fully capture the complications of infection-related physiological changes. To address these limitations, vital signs and a broad range of hematological and biochemical biomarkers are integrated with machine learning and explainable artificial intelligence (XAI). The data set used in this study was collected from the Kaggle data source. The dataset consists of vital sign values, such as body temperature, systolic and diastolic blood pressure, respiratory rate, heart rate, and oxygen saturation, along with blood-based biomarkers including albumin, base excess, bicarbonate, bilirubin, blast cells, calcium, creatinine, gamma-glutamyl transferase (GGT), glucose, hematocrit, hemoglobin, lactate, leukocytes, neutrophils, C-reactive protein (CRP), platelets, potassium, sodium, alanine aminotransferase (TGP\/ALT), activated partial thromboplastin time (TTPA), and urea. These parameters provide a complete view of the patient\u2019s physiological and biochemical state during infection. Feature selection was performed using a hybrid approach combining correlation filtering, mutual information, tree-based feature importance, and XAI validation (SHAP, permutation sensitivity) to ensure both predictive accuracy and interpretability. The integration of these techniques supports accurate classification and AI-assisted decision-making. The findings of this study highlight the importance of integrating both vital sign monitoring and laboratory assessments for effective infectious disease management.<\/jats:p>","DOI":"10.3390\/info17030227","type":"journal-article","created":{"date-parts":[[2026,2,27]],"date-time":"2026-02-27T11:22:57Z","timestamp":1772191377000},"page":"227","update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Explainable AI-Integrated Stacked Machine-Learning Model for Detection of Infectious Conditions Utilizing Vital Signs and Hematological Biomarkers"],"prefix":"10.3390","volume":"17","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0007-4892-2687","authenticated-orcid":false,"given":"Savithri","family":"Prabhu","sequence":"first","affiliation":[{"name":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9710-2289","authenticated-orcid":false,"given":"Giliyar Muralidhar","family":"Bairy","sequence":"additional","affiliation":[{"name":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-3345-360X","authenticated-orcid":false,"given":"Niranjana","family":"Sampathila","sequence":"additional","affiliation":[{"name":"Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"BRP Siddarama Dhruva","family":"Darshan","sequence":"additional","affiliation":[{"name":"Department of Computer Science and Engineering, M. S. Ramaiah University of Applied Sciences, Bengaluru 560058, India"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1968","published-online":{"date-parts":[[2026,2,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"100290","DOI":"10.1016\/j.health.2023.100290","article-title":"A predictive analytics model using machine learning algorithms to estimate the risk of shock development among dengue patients","volume":"5","author":"Chaw","year":"2024","journal-title":"Healthc. 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