Skip to main page content
U.S. flag

An official website of the United States government

Dot gov

The .gov means it’s official.
Federal government websites often end in .gov or .mil. Before sharing sensitive information, make sure you’re on a federal government site.

Https

The site is secure.
The https:// ensures that you are connecting to the official website and that any information you provide is encrypted and transmitted securely.

Access keys NCBI Homepage MyNCBI Homepage Main Content Main Navigation
. 2023;27(3):697-713.
doi: 10.1007/s00779-020-01475-3. Epub 2020 Nov 16.

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Samira Akhbarifar  1 , Hamid Haj Seyyed Javadi  2 , Amir Masoud Rahmani  1 , Mehdi Hosseinzadeh  3   4
Affiliations

A secure remote health monitoring model for early disease diagnosis in cloud-based IoT environment

Samira Akhbarifar et al. Pers Ubiquitous Comput. 2023.

Abstract

Internet of Things (IoT) and smart medical devices have improved the healthcare systems by enabling remote monitoring and screening of the patients' health conditions anywhere and anytime. Due to an unexpected and huge increasing in number of patients during coronavirus (novel COVID-19) pandemic, it is considerably indispensable to monitor patients' health condition continuously before any serious disorder or infection occur. According to transferring the huge volume of produced sensitive health data of patients who do not want their private medical information to be revealed, dealing with security issues of IoT data as a major concern and a challenging problem has remained yet. Encountering this challenge, in this paper, a remote health monitoring model that applies a lightweight block encryption method for provisioning security for health and medical data in cloud-based IoT environment is presented. In this model, the patients' health statuses are determined via predicting critical situations through data mining methods for analyzing their biological data sensed by smart medical IoT devices in which a lightweight secure block encryption technique is used to ensure the patients' sensitive data become protected. Lightweight block encryption methods have a crucial effective influence on this sort of systems due to the restricted resources in IoT platforms. Experimental outcomes show that K-star classification method achieves the best results among RF, MLP, SVM, and J48 classifiers, with accuracy of 95%, precision of 94.5%, recall of 93.5%, and f-score of 93.99%. Therefore, regarding the attained outcomes, the suggested model is successful in achieving an effective remote health monitoring model assisted by secure IoT data in cloud-based IoT platforms.

Keywords: Block encryption; Data mining; Health monitoring systems; Internet of Things; Security.

PubMed Disclaimer

Conflict of interest statement

Conflict of interestThe authors declare that they have no conflict interest.

Figures

Fig. 1
Fig. 1
The proposed secure remote health monitoring model in cloud-based IoT environment
Fig. 2
Fig. 2
Workflow graph of the suggested secure health monitoring model
Fig. 3.
Fig. 3.
The workflow of diagnosing the combinations of HCLS, HTN, and HD
Fig. 4
Fig. 4
Accuracy for different folds
Fig. 5
Fig. 5
Precision for different folds
Fig. 6
Fig. 6
Recall for different folds
Fig. 7
Fig. 7
F-score for different folds
See this image and copyright information in PMC

References

    1. Clerkin KJ, Fried JA, Raikhelkar J, Sayer G, Griffin JM, Masoumi A, Jain SS, Burkhoff D, Kumaraiah D, Rabbani LR, Schwartz A, Uriel N. COVID-19 and cardiovascular disease. Circulation. 2020;141:1648–1655. doi: 10.1161/CIRCULATIONAHA.120.046941. - DOI - PubMed
    1. Bansal M (2020) “Cardiovascular disease and COVID-19,” Diabetes & Metabolic Syndrome: Clin Res Rev - PMC - PubMed
    1. Zaki N, Alashwal H, Ibrahim S. Association of hypertension, diabetes, stroke, cancer, kidney disease, and high-cholesterol with COVID-19 disease severity and fatality: A systematic review. Diabetes Metab Syndr Clin Res Rev. 2020;14:1133–1142. doi: 10.1016/j.dsx.2020.07.005. - DOI - PMC - PubMed
    1. Ting DSW, Carin L, Dzau V, Wong TY. Digital technology and COVID-19. Nat Med. 2020;26:459–461. doi: 10.1038/s41591-020-0824-5. - DOI - PMC - PubMed
    1. Vaishya R, Javaid M, Khan IH, Haleem A (2020) “Artificial intelligence (AI) applications for COVID-19 pandemic,” Diabetes Metab Syndrome: Clin Res Rev - PMC - PubMed

LinkOut - more resources