ChronoSense: AI-Based Chronic Disease Prediction Using Wearable Sensor Data

ChronoSense presents a comprehensive review of artificial intelligence-driven methodologies applied to the prediction and early detection of chronic diseases through wearable sensor data. This paper systematically examines the intersection of continuous physiological monitoring, machine learning, and deep learning frameworks, exploring how temporal patterns captured by wearable devices — including heart rate variability, glucose levels, physical activity, and sleep metrics — can be leveraged to identify disease onset and progression. The review covers state-of-the-art predictive models, benchmark datasets, feature extraction techniques, and real-world deployment challenges such as data privacy, sensor noise, and model interpretability. By synthesizing findings across conditions including diabetes, cardiovascular disease, and respiratory disorders, ChronoSense aims to provide researchers and clinicians with a structured understanding of current capabilities, limitations, and future directions in AI-powered chronic disease surveillance. “This paper presents a comprehensive review of AI-based chronic disease prediction systems using wearable sensor data…”