Machine Learning Models for Early Disease Detection Using Wearable Biosensor Data in Personalized Healthcare

Abstract

Healthcare is shifting from hospital-centered treatment to continuous, preventive, and personalized care supported by digital technologies. Wearable biosensors such as smartwatches, patches, rings, textile sensors, and portable electrochemical devices generate real-time physiological data that can reveal subtle changes before symptoms become clinically severe. Machine learning models have become essential for transforming these high-volume and heterogeneous data streams into actionable medical insights. This paper investigates machine learning models for early disease detection using wearable biosensor data in personalized healthcare. It examines sensor modalities including heart rate, electrocardiography, blood oxygen saturation, skin temperature, glucose, motion signals, sleep patterns, and stress indicators. The study analyzes supervised, unsupervised, deep learning, and federated learning approaches for detecting cardiovascular disease, diabetes complications, respiratory infections, neurological disorders, sleep abnormalities, and mental health risks. Particular attention is given to signal preprocessing, multimodal fusion, anomaly detection, interpretability, and privacy-preserving analytics. Benefits include earlier intervention, reduced hospitalization, remote monitoring, improved adherence, and individualized treatment planning. Major challenges include noisy data, algorithmic bias, battery constraints, interoperability gaps, clinical validation, and ethical concerns surrounding surveillance and data ownership. A future roadmap is proposed involving digital twins, edge AI, generative health assistants, and adaptive closed-loop therapeutic systems. The paper concludes that wearable biosensor intelligence can significantly improve healthcare outcomes when robust models are combined with clinical oversight, secure data governance, and equitable access across diverse populations.