A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment.
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| Title: | A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment. |
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| Authors: | Verma, Ankit1 (AUTHOR) ankit.mca4u@gmail.com, Gupta, Amit Kumar1 (AUTHOR) amit.gupta@kiet.edu, Kumar, Vipin1 (AUTHOR) vipin.kumar.mca@kiet.edu, Rajak, Akash1 (AUTHOR) akashrajak@gmail.com, Kumar, Sushil2 (AUTHOR) sushil.kumar@kiet.edu, Panda, Rabi Narayan1 (AUTHOR) rn.panda@kiet.edu |
| Source: | Multimedia Tools & Applications. Feb2025, Vol. 84 Issue 7, p3767-3792. 26p. |
| Subjects: | Approximate reasoning, Hypertension, Diagnosis, Digital health, Internet of things |
| Abstract: | People who lead hectic lives daily suffer from a variety of illnesses, including diabetes, high blood pressure, hypertension, etc. For someone to survive, they must become aware of these illnesses promptly. The Internet of Things (IoT) and cloud computing are the two critical prerequisites for digital healthcare. In the present research, the attacked data are detected and removed using the security module to enhance the security of the healthcare system. However, an accurate prediction mechanism is needed for the early diagnosis of the diseases. To predict the sickness and its severity more accurately, a unique Dragon Fly-based Generalised Approximate Reasoning Intelligence Control (DF-GARIC) is devised in this article. This system was primarily responsible for preprocessing the cloud medical records entered into the system. Additionally, the regression algorithm extracts the relevant features. Based on the retrieved features, the dragonfly function is used to classify the disease and estimate its severity. Subsequently, a warning is given to the providers for the abnormal condition via SMS or e-mail. The system validated a higher accuracy level of 99.8% from the MATLAB execution. [ABSTRACT FROM AUTHOR] |
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| Database: | Engineering Source |
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| Abstract: | People who lead hectic lives daily suffer from a variety of illnesses, including diabetes, high blood pressure, hypertension, etc. For someone to survive, they must become aware of these illnesses promptly. The Internet of Things (IoT) and cloud computing are the two critical prerequisites for digital healthcare. In the present research, the attacked data are detected and removed using the security module to enhance the security of the healthcare system. However, an accurate prediction mechanism is needed for the early diagnosis of the diseases. To predict the sickness and its severity more accurately, a unique Dragon Fly-based Generalised Approximate Reasoning Intelligence Control (DF-GARIC) is devised in this article. This system was primarily responsible for preprocessing the cloud medical records entered into the system. Additionally, the regression algorithm extracts the relevant features. Based on the retrieved features, the dragonfly function is used to classify the disease and estimate its severity. Subsequently, a warning is given to the providers for the abnormal condition via SMS or e-mail. The system validated a higher accuracy level of 99.8% from the MATLAB execution. [ABSTRACT FROM AUTHOR] |
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| ISSN: | 13807501 |
| DOI: | 10.1007/s11042-024-19131-w |
