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1 *Corresponding author: [email protected]
2
Medical Data Classification in Cloud Computing Using Soft Computing With Voting Classifier: A Review
Saurabh Sharma1*, Harish K. Shakya1† and Ashish Mishra2‡
1Dept. of CSE, Amity School of Engineering & Technology, Amity University (M.P.), Gwalior, India
2Department of CSE, Gyan Ganga Institute of Technology and Sciences, Jabalpur, India
Abstract
In the current context, a tele-medical system is the rising medical service where health professionals can use telecommunication technology to treat, evaluate, and diagnose a patient. The data in the healthcare system signifies a set of medical data that is sophisticated and larger in number (X-ray, fMRI data, scans of the lungs, brain, etc.). It is impossible to use typical hardware and software to manage medical data collections. Therefore, a practical approach to the equilibrium of privacy protection and data exchange is required. To address these questions, several approaches are established, most of the studies focusing on only a tiny problem with a single notion. This review paper analyzes the data protection research carried out in cloud computing systems and also looks at the major difficulties that conventional solutions confront. This approach helps researchers to better address existing issues in protecting the privacy of medical data in the cloud system.
Keywords: Medical data, soft computing, fuzzy, cloud computing, data privacy, SVM, FCM
2.1 Introduction
There are many definitions in Electronic Health Record (EHR), such as the electronic record that holds patient information on a health record system operated by healthcare providers [1]. Although EHR has a good effect on healthcare services, development in many healthcare institutions globally, particularly in poor nations, is delayed due to numerous common problems. Patient data security has been a problem since the beginning of medical history and is an important issue in modern day. Initiated by the idea of confidentiality, the Oath of Hippocrates has proved to be an honorable activity in clinical and medical ethics. It is of highest importance to protect the privacy and confidentiality of patient information; security is trustworthy. Medical record security generally involves privacy and confidentiality [2]. Cloud computing provides the option of accessing massive amounts of patient information in a short period of time. This makes it easier for an unauthorized person to obtain patient records. It confirms this feeling by saying “illegal access to traditional medical records (paper-based) has always been conceivable, but computer introduction increases a little problem to a large problem.”
Cloud computing is a concept for easy, on-demand access to a common pool of configurable computer resources (e.g., networks, servers, storage, applications, and services), which may easily be provided and disclosed with minimal administration effort or engagement from service providers [4]. The newest, most exciting, and comprehensive solution in the world of IT is cloud computing. Its major purpose is to use the Internet or intranet to exchange resources for users [5]. Cloud computing is an affordable, automatically scalable, multi-tenant, and secure cloud service provider platform (CSP).
2.1.1 Security in Medical Big Data Analytics
Big data is complex and uncomplicated by its very nature and requires suppliers to take a close look at their techniques to collection, storage, analysis, and presentation of their data to personnel, business partners, and patients.
What are some of the most challenging tasks for enterprises when starting up a big data analytics program, and how can they overcome these problems to reach their clinical and financial goals?
2.1.1.1 Capture
All data comes from someone, but regrettably, it is not always from someone with flawless data management habits for many healthcare providers. Collecting clean, comprehensive, precise, and correctly structured data for numerous systems is a constant battle for businesses, many of whom are not on the gaining side of the conflict.
In a recent investigation at an ophthalmology clinic, EHR data were only 23.5% matched by patient-reporting data. When patients reported three or more eye problems, their EHR data were absolutely not in agreement.
Poor usability of EHRs, sophisticated processes, and an incomplete understanding why big data is crucial to properly collect all can contribute to quality problems that afflict data during its life cycle.
Providers can begin
