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

Q1 Decision Sciences
Shabanam K. Shikalgar, N. V. S. Pavan Kumar, Gavendra Singh, Faizur Rashid
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引用次数: 0

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.

基于分数金枪鱼旗鱼优化的云深度残差网络隐私保护医疗数据分类
如今,随着新兴技术的发展,人们越来越关注隐私保护医疗数据的分类和各种隐私保护模型的开发,以促进在线医疗预诊断系统的发展。医疗数据是高度敏感的,确保第三方用户的医疗记录隐私对于提高服务质量、满足患者并赢得信任至关重要。医学保存数据的分类有助于根据患者的疾病和症状对患者进行分类,从而建立临床决策系统。本文设计了一种基于混合优化的深度学习模型——分数金枪鱼旗鱼优化-深度残差网络(Fractional Tuna Sailfish Optimization-Deep Residual Network, FractionalTSFO-DRN),用于对隐私保存的医疗数据进行精确分类。采用金枪鱼旗鱼优化(TSFO)算法生成关键矩阵,利用隐私效用系数矩阵来保证医疗数据的隐私性。在DRN的分类过程中允许医疗数据的隐私保护,并利用引入的分数阶TSFO对DRN中的分类进行优化和增强。评估后使用心脏病预测数据库,结果表明,采用的分类技术准确率为94.67%,真阳性率为93.56%,真阴性率为89.68%。
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来源期刊
Annals of Data Science
Annals of Data Science Decision Sciences-Statistics, Probability and Uncertainty
CiteScore
6.50
自引率
0.00%
发文量
93
期刊介绍: Annals of Data Science (ADS) publishes cutting-edge research findings, experimental results and case studies of data science. Although Data Science is regarded as an interdisciplinary field of using mathematics, statistics, databases, data mining, high-performance computing, knowledge management and virtualization to discover knowledge from Big Data, it should have its own scientific contents, such as axioms, laws and rules, which are fundamentally important for experts in different fields to explore their own interests from Big Data. ADS encourages contributors to address such challenging problems at this exchange platform. At present, how to discover knowledge from heterogeneous data under Big Data environment needs to be addressed.     ADS is a series of volumes edited by either the editorial office or guest editors. Guest editors will be responsible for call-for-papers and the review process for high-quality contributions in their volumes.
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