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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References
Adel A (2020) Utilizing technologies of fog computing in educational IoT systems: privacy, security, and agility perspective. J Big Data 7(1):1–29
Al-Joboury IM, Al-Hemiary EH (2017) F2CDM: internet of things for healthcare network based fog-to-cloud and data-in-motion using MQTT protocol. In: International symposium on ubiquitous networking. Springer, Cham, pp 368–379
Catarinucci L, De Donno D, Mainetti L, Palano L, Patrono L, Stefanizzi ML, Tarricone L (2015) An IoT-aware architecture for smart healthcare systems. IEEE Internet Things J 2(6):515–526
Cech HL, Grossmann M, Krieger UR (2019) A fog computing architecture to share sensor data by means of blockchain functionality. 2019 IEEE Int Conf Fog Comput:31–40
Darshan KR, Anandakumar KR (2015) A comprehensive review on usage of internet of things (IoT) in healthcare system. In: 2015 international conference on emerging research in electronics, computer science and technology (ICERECT). IEEE, pp 132–136
de Macedo DDJ, de Araújo GM, Dutra ML, Dutra ST, Lezana ÁGR (2019) Toward an efficient healthcare CloudIoT architecture by using a game theory approach. Concurr Eng 27(3):189–200
Dhar SK, Bhunia SS, Mukherjee N (2014) Interference aware scheduling of sensors in IoT enabled health-care monitoring system. In: 2014 fourth international conference of emerging applications of information technology. IEEE, pp 152–157
Dziak D, Jachimczyk B, Kulesza WJ (2017) IoT-based information system for healthcare application: design methodology approach. Appl Sci 7(6):596
Ganesan M, Sivakumar N (2019) IoT based heart disease prediction and diagnosis model for healthcare using machine learning models. In 2019 IEEE international conference on system, computation, automation and networking (ICSCAN). IEEE, pp 1–5
Hang L, Kim D-H (2019) Design and implementation of an integrated IoT blockchain platform for sensing data integrity. Sensors 19(10):22–28
Jagadeeswari V, Subramaniyaswamy V, Logesh R, Vijayakumar V (2018) A study on medical internet of things and big data in personalized healthcare system. Health Inf Sci Syst 6(1):1–20
Kodali RK, Swamy G, Lakshmi B (2015) An implementation of IoT for healthcare. In: 2015 IEEE recent advances in intelligent computational systems (RAICS). IEEE, pp 411–416
Kumar Y, Mahajan M (2019) Intelligent behavior of fog computing with IOT for healthcare system. Int J Sci Technol Res 8(07)
Latha CBC, Jeeva SC (2019) Improving the accuracy of prediction of heart disease risk based on ensemble classification techniques. Inf Med Unlocked 16:100203
Machado JDS, Moreno ED, Ribeiro ADRL (2020) A survey on fog computing and its research challenges. Int J Grid and Utility Comput 11(4):486–495
Mani N, Singh A, Nimmagadda SL (2020) An IoT guided healthcare monitoring system for managing real-time notifications by fog computing services. Procedia Comp Sci 167:850–859
Muthanna A, … Koucheryavy A (2019) Secure and reliable IoT networks using fog computing with software-defined networking and blockchain. J Sens Actuator Netw 8(1):15
Rahmani AM, Gia TN, Negash B, Anzanpour A, Azimi I, Jiang M, Liljeberg P (2018) Exploiting smart e-health gateways at the edge of healthcare internet-of-things: a fog computing approach. Futur Gener Comput Syst 78:641–658
Rotariu C, Manta V (2012) Wireless system for remote monitoring of oxygen saturation and heart rate. In: 2012 federated conference on computer science and information systems (FedCSIS). IEEE, pp 193–196
Satpathy S, Mohan P, Das S, Debbarma S (2020) A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA. J Supercomput 76(8):5849–5861
Tuli S, Basumatary N, Gill SS, Kahani M, Arya RC, Wander GS, Buyya R (2020) HealthFog: an ensemble deep learning based smart healthcare system for automatic diagnosis of heart diseases in integrated IoT and fog computing environments. Futur Gener Comput Syst 104:187–200 https://archive.ics.uci.edu/ml/datasets/heart+disease
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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