DIGITAL BIOMARKER MODELING FOR EARLY DISEASE DETECTION

Abstract

Digital biomarker modeling has emerged as a promising approach for early disease detection by enabling continuous, objective, and data-driven health assessment. This study explores advanced modeling techniques for extracting clinically meaningful digital biomarkers from heterogeneous data sources, including wearable sensors, mobile health applications, and electronic health records. Machine learning and signal processing methods are applied to identify subtle physiological and behavioral patterns that precede the onset of disease. The proposed framework emphasizes robustness, interpretability, and scalability, allowing early detection of pathological changes before clinical symptoms become evident. Experimental results demonstrate that digital biomarker-based models significantly improve prediction accuracy compared to traditional diagnostic approaches. The findings highlight the potential of digital biomarkers to support proactive healthcare, personalized intervention strategies, and improved clinical decision-making. This research contributes to the growing field of digital medicine by providing a systematic and adaptable model for early disease detection across diverse clinical contexts.