Smart Healthcare System for Real-Time Patient Monitoring & Fault Prediction Using IoT & Machine Learning

DOI

10.2991/978-94-6463-738-0_61How to use a DOI?

Keywords

Internet of Things (IoT); Machine Learning (ML); Real-Time Monitoring; Patient Health Tracking; Predictive Healthcare; Fault Prediction; Smart Healthcare System; Saline Monitoring

Abstract

Integrating Machine Learning (ML) and the Internet of Things (IoT) into healthcare delivers transformative potential by enabling real-time monitoring and predictive care. This study introduces a smart healthcare system that employs IoT sensors to monitor vital parameters such as heart rate, oxygen saturation, body temperature, and saline bottle levels. The collected data is processed through advanced ML algorithms to analyse patterns and detect anomalies, ensuring early identification of health risks. The system combines IoT-enabled real-time data collection, ML-based anomaly detection, and cloud-based storage for secure data handling and visualization. A responsive dashboard provides healthcare professionals with actionable insights and instant alerts, enabling timely medical interventions and enhancing decision-making. Key findings demonstrate the system’s ability to improve patient safety, reduce human error, and optimize resource allocation by automating monitoring processes. This innovation improves operational efficiency, enhances patient outcomes, and lowers healthcare costs, making it particularly beneficial for resource-limited settings. By integrating advanced technology, the system represents a significant step toward preventive and personalized healthcare.

Copyright

© 2025 The Author(s)

Open Access

Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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TY  - CONF
AU  - Roshan Gupta
AU  - Shivani Srivastava
AU  - Soham Rayewar
AU  - Siddhasen Patil
PY  - 2025
DA  - 2025/06/22
TI  - Smart Healthcare System for Real-Time Patient Monitoring & Fault Prediction Using IoT & Machine Learning
BT  - Proceedings of the International Conference on Advances and Applications in Artificial Intelligence (ICAAAI 2025)
PB  - Atlantis Press
SP  - 767
EP  - 780
SN  - 1951-6851
UR  - https://doi.org/10.2991/978-94-6463-738-0_61
DO  - 10.2991/978-94-6463-738-0_61
ID  - Gupta2025
ER  -