A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment.
Saved in:
| Title: | A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment. |
|---|---|
| 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] |
| Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
|
Full text is not displayed to guests.
Login for full access.
|
|
| FullText | Links: – Type: pdflink Text: Availability: 1 |
|---|---|
| Header | DbId: egs DbLabel: Engineering Source An: 182974895 AccessLevel: 6 PubType: Academic Journal PubTypeId: academicJournal PreciseRelevancyScore: 0 |
| IllustrationInfo | |
| Items | – Name: Title Label: Title Group: Ti Data: A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Verma%2C+Ankit%22">Verma, Ankit</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> ankit.mca4u@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Gupta%2C+Amit+Kumar%22">Gupta, Amit Kumar</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> amit.gupta@kiet.edu</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Vipin%22">Kumar, Vipin</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> vipin.kumar.mca@kiet.edu</i><br /><searchLink fieldCode="AR" term="%22Rajak%2C+Akash%22">Rajak, Akash</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> akashrajak@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Kumar%2C+Sushil%22">Kumar, Sushil</searchLink><relatesTo>2</relatesTo> (AUTHOR)<i> sushil.kumar@kiet.edu</i><br /><searchLink fieldCode="AR" term="%22Panda%2C+Rabi+Narayan%22">Panda, Rabi Narayan</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> rn.panda@kiet.edu</i> – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22Multimedia+Tools+%26+Applications%22">Multimedia Tools & Applications</searchLink>. Feb2025, Vol. 84 Issue 7, p3767-3792. 26p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Approximate+reasoning%22">Approximate reasoning</searchLink><br /><searchLink fieldCode="DE" term="%22Hypertension%22">Hypertension</searchLink><br /><searchLink fieldCode="DE" term="%22Diagnosis%22">Diagnosis</searchLink><br /><searchLink fieldCode="DE" term="%22Digital+health%22">Digital health</searchLink><br /><searchLink fieldCode="DE" term="%22Internet+of+things%22">Internet of things</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: 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] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of Multimedia Tools & Applications is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
| PLink | https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=182974895 |
| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s11042-024-19131-w Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 26 StartPage: 3767 Subjects: – SubjectFull: Approximate reasoning Type: general – SubjectFull: Hypertension Type: general – SubjectFull: Diagnosis Type: general – SubjectFull: Digital health Type: general – SubjectFull: Internet of things Type: general Titles: – TitleFull: A Secure Healthcare Monitoring System for Disease Diagnosis in the IoT Environment. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Verma, Ankit – PersonEntity: Name: NameFull: Gupta, Amit Kumar – PersonEntity: Name: NameFull: Kumar, Vipin – PersonEntity: Name: NameFull: Rajak, Akash – PersonEntity: Name: NameFull: Kumar, Sushil – PersonEntity: Name: NameFull: Panda, Rabi Narayan IsPartOfRelationships: – BibEntity: Dates: – D: 25 M: 02 Text: Feb2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 13807501 Numbering: – Type: volume Value: 84 – Type: issue Value: 7 Titles: – TitleFull: Multimedia Tools & Applications Type: main |
| ResultId | 1 |
