Classification of Privacy Preserved Medical Data with Fractional Tuna Sailfish Optimization Based Deep Residual Network in Cloud

Shabanam K. Shikalgar (), N. V. S. Pavan Kumar, Gavendra Singh and Faizur Rashid
Additional contact information
Shabanam K. Shikalgar: Sharad Institute of Technology College of Engineering
N. V. S. Pavan Kumar: Koneru Lakshmiah Education Foundation
Gavendra Singh: College of Computing and Informatics Haramaya University
Faizur Rashid: College of Computing and Informatics Haramaya University

Annals of Data Science, 2025, vol. 12, issue 3, No 1, 829-854

Abstract: Abstract Nowadays, with the growth of emerging technologies, increased attention has been paid to the classification of privacy-preserved medical data and development of various privacy-preserving models for the promotion of online medical pre-diagnosis systems. Medical data is highly sensitive and it is essential to ensure privacy of medical records from third-party users to increase service quality, satisfy patients and earn trust. The classification of medical preserved data is helpful to build a clinical decision system by classifying patients based on their disease and symptoms. In this article, a hybrid optimization-based deep learning model named Fractional Tuna Sailfish Optimization–Deep Residual Network (FractionalTSFO-DRN) is designed to precisely classify the privacy preserved medical data. A privacy utility coefficient matrix is used to ensure the privacy of medical data by generating a key matrix using Tuna Sailfish Optimization (TSFO) algorithmic technique. The privacy-preserved medical data is allowed for the classification process using DRN and the introduced Fractional TSFO is used to optimize and enhance the classification in DRN. The assessment followed by using heart disease prediction databases proved that the employed classification technique recorded an accuracy of 94.67%, a True Positive Rate of 93.56%, and a True Negative Rate of 89.68% respectively.

Keywords: Tuna swarm optimization; Sailfish optimization; Deep residual network; Fractional calculus; Privacy preserved medical data (search for similar items in EconPapers)
Date: 2025
References: Add references at CitEc
Citations:

Downloads: (external link)
http://link.springer.com/10.1007/s40745-024-00538-z 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:aodasc:v:12:y:2025:i:3:d:10.1007_s40745-024-00538-z

Ordering information: This journal article can be ordered from
https://www.springer ... gement/journal/40745

DOI: 10.1007/s40745-024-00538-z

Access Statistics for this article

Annals of Data Science is currently edited by Yong Shi

More articles in Annals of Data Science from Springer
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().