Abstract
Smart healthcare serves as an innovative way for integrating sensors, Internet of things (IoT) and large scale analytics. A better patient monitoring reduces the costs of medical services to great extent, which opens up new frontiers or strategies for the sustainability of mankind in terms of data services, remote diagnosing/monitoring or the new kinds of treatments. However, Cloud computing is an on-demand service that provides storage, network, power to do intensive computing, intensive sharing of resources, but users should also deal with the privacy-related issues to the data stored in the cloud through data breaches. The current health care systems implemented on the cloud poses threat to the privacy of the data of the patients, since data breaches cost millions to billions of dollars to the health care institutions every year this problem can be taken care with the Fog computing (FC) based health care systems, where the fog technology offers more additional advantages like low latency, resource management, less power consumption, etc. The proposed system demonstrates the use of IoT, computing platforms (cloud/Fog) with Machine Learning (ML) algorithms. Fog layer extracts the attributes and filters the data collected about heart diseases, whereas cloud layer estimates the level of disease detection using the classifier algorithms Random forest and Naive Bayes. In terms of accuracy, precision, recall, and f-measure, the results suggest that Random Forest outperforms Naive Bayes.
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Hanumantharaju, R., Shreenath, K.N., Sowmya, B.J. et al. Fog based smart healthcare: a machine learning paradigms for IoT sector. Multimed Tools Appl 81, 37299–37318 (2022). https://doi.org/10.1007/s11042-022-13530-7
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DOI: https://doi.org/10.1007/s11042-022-13530-7