Development of Cost-effective, Reliable, and Efficient Cloud-Based Model for Health Monitoring Using IoT
DOI:
https://doi.org/10.31305/rrijm2024.v04.n03.008Keywords:
IoT, Cloud Computing, K-Means, KNN, Health Monitoring, ECG, Machine Learning, Cloud ArchitectureAbstract
The rapid evolution of Internet of Things (IoT) and cloud computing has revolutionized the healthcare domain by enabling continuous, real-time, and remote monitoring of patients. However, existing systems often suffer from high implementation costs, limited scalability, and unreliable data processing. This research proposes a cost-effective, reliable, and efficient cloud-based health monitoring model that integrates IoT devices with machine learning techniques for predictive healthcare analytics. The system uses K-Means clustering to detect anomalies in physiological data and K-Nearest Neighbors (KNN) for classification to identify health conditions accurately. Experimental results on a real ECG dataset (4,998 samples) show 98.30% classification accuracy, 98.75% training accuracy, and a Silhouette Score of 0.37, indicating moderately distinct clusters. The model offers real-time, scalable, and adaptable performance suitable for deployment in both clinical and remote healthcare settings.
References
J. Mohammed et al., "Internet of Things: Remote patient monitoring using web services and cloud computing," IEEE iThings, 2014. DOI: https://doi.org/10.1109/iThings.2014.45
M. Hassanalieragh et al., "Health Monitoring and Management Using Internet-of-Things (IoT) Sensing with Cloud-Based Processing," IEEE Services Computing, 2015. DOI: https://doi.org/10.1109/SCC.2015.47
S. Selvaraj and S. Sundaravaradhan, "Challenges and opportunities in IoT healthcare systems: A systematic review," SN Applied Sciences, 2020. DOI: https://doi.org/10.1007/s42452-019-1925-y
M.A. Panhwar et al., "Energy-efficient routing optimization algorithm in WBANs for patient monitoring," J. Ambient Intell. Humaniz. Comput., 2021. DOI: https://doi.org/10.1007/s12652-020-02541-7
M.U. Ahmed et al., "An overview on IoT for health monitoring systems," ICST Institute of Computer Science, 2016. DOI: https://doi.org/10.1007/978-3-319-47063-4_44
B. Talbot et al., "Remote patient monitoring systems: Applications, architecture, and challenges," Can. J. Kidney Health Dis., 2022.
S. Tyagi, A. Agarwal, and P. Maheshwari, "A conceptual framework for IoT-based healthcare system using cloud computing," IEEE Confluence, 2016. DOI: https://doi.org/10.1109/CONFLUENCE.2016.7508172
Satish Polshettiwar et al., "An Overview on Cloud-Based Health Tracking and Monitoring System in Real-Time," IJMRHS, 2022.
U. K. Padyana, H. P. Rai, P. Ogeti, N. S. Fadnavis, G. B. Patil, “AI and Machine Learning in Cloud-Based Internet of Things (IoT) Solutions: A Comprehensive Review and Analysis,” Integrated Journal for Research in Arts and Humanities, vol. 3, no. 3, pp. 121–132, May 2023. DOI: https://doi.org/10.55544/ijrah.3.3.20
