FURI | Spring 2025

Supervised Learning for Early Detection of Chronic Diseases Using Wearable and Other Health Data in NHANES

With the rise of fitness trackers, identifying meaningful patterns is crucial for detecting early indicators of chronic diseases such as pre-diabetes mellitus. This approach leverages wearable and other health data from the National Health and Nutrition Examination Survey (NHANES) to develop a Supervised Learning (SL) framework that detects pre-diabetes mellitus symptoms using hypertension, sleep and physical activity measurements from wearables. Exploratory analysis is conducted for data preprocessing and visualization, and several benchmark SL methods are explored. This study provides a data-driven approach for early detection of chronic diseases, leading to effective behavioral changes, improved patient outcomes and a healthier society.