ETIOLOGICAL IDENTIFICATION OF RESPIRATORY AND ANGINA SYNDROMES USING ARTIFICIAL INTELLIGENCE BASED ON CLINICAL SYMPTOMS AND LABORATORY MARKERS

Infectious diseases presenting with respiratory and angina syndromes are widely prevalent in clinical practice, and early and accurate etiological diagnosis is crucial for selecting effective treatment strategies. This study evaluates the effectiveness of artificial intelligence (AI) technologies in etiological diagnosis based on clinical symptoms and laboratory markers. Machine learning models (random forest, gradient boosting, logistic regression) were applied for multiparametric analysis, demonstrating that integration of CRP, procalcitonin, leukocyte and lymphocyte counts, and clinical features enables high diagnostic accuracy (AUC ≥ 0.94) in differentiating bacterial and viral etiologies. The AI-based approach reduces diagnostic errors and optimizes clinical decision-making.