Paper Info
Title
Heart Disease Data Based Privacy Preservation Using Enhanced Elgamal And Resnet Classifier
Abstract
Heart disease is increasing, and their detection is a significant concern. With the several technologies developed, the approaches for detection mechanisms can be improved further with better and improved algorithms. Data related with patient's health are of huge amount and stored in the larger space of cloud storage. Accessing cloud storage is an easy task, where the data stored is available to many users of cloud and there comes the need of security. Generally security can be improved using encryption and decryption algorithms. In this paper, a framework using the ResNet-50 classifier approach for secured transmission of heart disease features is performed. This study focused on an enhanced ElGamal encryption-decryption method for the encryption of data with a generated private key and a public key for decryption to better access the data. The data encrypted are then decrypted when the user request data. With Convolutional Neural Network classifier of ResNet-50 with its near 50 layers, the refinement or classification process is performed. The heart disease dataset from the UCI heart disease repository is considered for the evaluation of the proposed work. Further, with feature selection method, better selection of input can be filtered from the selected dataset. The results are obtained with respect to various performance measures, then compared and analyzed with some of the existing methodologies. The results proved to be better than other existing frameworks.
Year
DOI
Venue
2022
10.1016/j.bspc.2021.103185
BIOMEDICAL SIGNAL PROCESSING AND CONTROL
Keywords
DocType
Volume
KNN, ResNet-50, ElGamal encryption, CNN, Feature selection
Journal
71
Issue
ISSN
Citations 
Part
1746-8094
0
PageRank 
References 
Authors
0.34
0
3
Name
Order
Citations
PageRank
V. Benhar Charles100.34
D. Surendran211.03
A. Sureshkumar300.68