A Deep Learning Framework for Non-Invasive Disease Detection Using Wearable Sensor Data and Neural Networks

Abstract

Non-invasive disease detection through wearable sensor data offers a promising avenue for proactive healthcare management. This paper proposes a deep learning framework leveraging neural networks to analyze physiological signals collected from wearable devices for the early detection of various diseases. The framework encompasses data collection, preprocessing techniques, a novel deep learning model architecture tailored for time-series sensor data, rigorous evaluation metrics, and a discussion of its potential and limitations. We demonstrate the efficacy of the proposed approach using publicly available and simulated wearable sensor datasets, showcasing its ability to achieve competitive performance in disease classification tasks.