Real-Time Liver Health Monitoring System Using Deep Learning and Wearable Sensors

Authors

  • Potluri Divya Sri Author
  • Prasanth Yalla Author

DOI:

https://doi.org/10.64252/1rxp7k49

Keywords:

Liver health monitoring, deep learning, wearable sensors, convolutional neural network, cloud computing, real-time diagnostics.

Abstract

This paper presents a novel real-time liver health monitoring system integrating wearable sensors, deep learning, and cloud computing for continuous, non-invasive assessment of liver function. Utilizing a convolutional neural network (CNN), the system analyses physiological data streams from wearable devices to predict liver health status with high accuracy. The implementation leverages a scalable cloud-based architecture and a mobile application for user feedback, addressing challenges in data accuracy, latency, and privacy. Compared to traditional diagnostic methods, this system offers timely anomaly detection and enhanced accessibility. Experimental results demonstrate a classification accuracy of 92%, surpassing existing statistical models. This work provides a robust framework for preventive liver healthcare, with potential for widespread adoption.

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Published

2025-09-08

Issue

Section

Articles

How to Cite

Real-Time Liver Health Monitoring System Using Deep Learning and Wearable Sensors. (2025). International Journal of Environmental Sciences, 2026-2036. https://doi.org/10.64252/1rxp7k49