Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning

Author:

Veeramakali T.1,Shobanadevi A.1ORCID,Nayak Nihar Ranjan2,Kumar Sumit3,Singhal Sunita4,Subramanian Manoharan5ORCID

Affiliation:

1. Department of Data Science and Business Systems, School of Computing, SRM Institute of Science and Technology, Kattankulathur, Chennai, India

2. Sri Venkateswara College of Engineering Technology, Chittoor, India

3. Indian Institute of Management, Kozhikode, India

4. Department of Computer Science and Engineering, Manipal University Jaipur, Jaipur 303007, India

5. Department of Computer Science, School of Informatics and Electrical Engineering, Hachalu Hundesa Campus, Ambo University, Ambo, Ethiopia

Abstract

A key challenge in clinical recommendation systems is the problem of aberrant patient profiles in social networks. As a result of a person’s abnormal profile, numerous vests might be used to make fake remarks about them, cyber bullying, or cyber-attacks. Many clinical researchers have done extensive study on this topic. The most recent studies on this topic are summarized, and an overarching framework is provided. When it comes to the methods and datasets that make up the data collection, the feature presentation and algorithm selection layers provide an overview of the various types of algorithm selections available. The categorization and evaluation of diseases and disorders has been one of the major advantages of machine learning in medical. Because it was harder to predict, it rendered it more controllable. It might range from difficult-to-find cancers in the early stages to certain other illnesses spread through the bloodstream. In healthcare, we may pick methods in machine learning depending on reliable outcomes. To do so, we must run the findings through each method. The major issue arises during information training and validation. Because the dataset is so large, eliminating mistakes might be difficult. The providers, other characteristics, various algorithms, data labelling techniques, and assessment criteria are all presented and contrasted in depth. Detecting anomalous users in medical social networks, on the other hand, is a work in progress. The result evaluation layer provides an explanation of how to evaluate and mark up the results of the various algorithm selection layers. Finally, it looks forward to more study in this area.

Publisher

Hindawi Limited

Subject

General Mathematics,General Medicine,General Neuroscience,General Computer Science

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Retracted: Preserving the Privacy of Healthcare Data over Social Networks Using Machine Learning;Computational Intelligence and Neuroscience;2023-07-26

2. Social Networks Analysis and Machine Learning: an Overview of Approaches and Applications;2023 International Conference on Sustainable Computing and Smart Systems (ICSCSS);2023-06-14

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