Explainable AI-Integrated Stacked Machine-Learning Model for Detection of Infectious Conditions Utilizing Vital Signs and Hematological Biomarkers

Savithri Prabhu, Giliyar Muralidhar Bairy, Niranjana Sampathila, BRP Siddarama Dhruva Darshan

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’s 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.