ARTIFICIAL INTELLIGENCE-BASED PREDICTION OF DISEASE SEVERITY AND DEVELOPMENT OF INDIVIDUALIZED TREATMENT STRATEGIES IN CHRONIC OBSTRUCTIVE PULMONARY DISEASE

Chronic Obstructive Pulmonary Disease (COPD) is a leading cause of morbidity and mortality worldwide. Early prediction of disease severity and optimization of individualized treatment strategies remain key challenges in clinical practice. This study aims to develop an artificial intelligence (AI)-based model for predicting COPD severity and to propose personalized treatment approaches based on integrated clinical, functional, and biomarker data. A total of 320 patients diagnosed with COPD were included in this prospective study. Clinical parameters, spirometry data, inflammatory biomarkers (CRP, IL-6, TNF-α), and imaging findings were analyzed. Machine learning algorithms, including Random Forest and XGBoost, were applied to develop predictive models. The AI model demonstrated high diagnostic performance (AUC = 0.92, sensitivity = 88%, specificity = 85%). Integration of biomarker and functional data significantly improved prediction accuracy. The proposed approach enables early identification of high-risk patients and supports personalized treatment decision-making.