Securing cloud-based medical data: an optimal dual kernal support vector approach for enhanced EHR management

M. L. Sworna Kokila (), E. Fenil (), N. P. Ponnuviji () and G. Nirmala ()
Additional contact information
M. L. Sworna Kokila: SRM Institute of Science and Technology
E. Fenil: Holycross Engineering College
N. P. Ponnuviji: RMK College of Engineering and Technology
G. Nirmala: R.M.D. Engineering College

International Journal of System Assurance Engineering and Management, 2024, vol. 15, issue 7, No 47, 3495-3507

Abstract: Abstract Cloud computing is one of the advanced technologies to process rapidly growing data. At the same instant, the necessity of storage space for the voluminous digital medical data has been amplified thanks to the mounting electronic health records. It influences the employment of cloud outsourcing methodology. Data outsourced to the cloud space must be highly secured. For this, the paper presents a DKS-CWH algorithm that is based on a dual kernal support vector (DKS) and crossover-based wild horse optimization algorithm. In this paper, the input grayscale images are gathered from the medical MINST dataset which includes 58,954 images comprising six classes of CXR (chest X-ray), breast MRI, abdomen CT, chest CT, hand (hand X-ray), and head CT. The classification and feature extraction processes are performed at the cloud layer using the DKS-CWH algorithm. The hyperparameters of the DKS approach are optimized with the crossover-based WHO algorithm. The performance evaluation involves analyzing its effectiveness according to prominent metrics such as precision, accuracy, recall, and F1-score and comparing the outputs with the other competent methods. The results showed the DKS-CWH model offered robust performance with 97% accuracy.

Keywords: Cloud computing; Dual kernel support vector machine; Wild horse optimization; Crossover; Data security (search for similar items in EconPapers)
Date: 2024
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s13198-024-02356-1 Abstract (text/html)
Access to the full text of the articles in this series is restricted.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:spr:ijsaem:v:15:y:2024:i:7:d:10.1007_s13198-024-02356-1

Ordering information: This journal article can be ordered from
http://www.springer.com/engineering/journal/13198

DOI: 10.1007/s13198-024-02356-1

Access Statistics for this article

International Journal of System Assurance Engineering and Management is currently edited by P.K. Kapur, A.K. Verma and U. Kumar

More articles in International Journal of System Assurance Engineering and Management from Springer, The Society for Reliability, Engineering Quality and Operations Management (SREQOM),India, and Division of Operation and Maintenance, Lulea University of Technology, Sweden
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().