Predictive data analysis approach for securing medical data in smart grid healthcare systems
Introduction
In the medical field, security is an essential part of securing patients’ data from third parties who could modify or delete a ‘patient’s record. To prevent this, proper methods are used to secure medical data [1]. If the data are transferred to a network, significant issues can arise, such as data loss, data delay, and attacks that lead to insecurity [2]. This must be avoided in the initial stage to achieve the original data transmission. By protecting data security, the health researchers access the data collection and storage and give the users the necessary treatment [3]. Medical data includes the “patient’s health care and treatments followed, and unauthorized user hacks can severely affect. The Health Insurance Portability and Accountability Act is used to secure patients’ health records [4]. According to this policy, the patients’ sensitive data is stored and cannot be accessed without the patient; medical data is also stored and exchanged in a secure manner [5].
The forensics medical data includes collecting healthcare records of patients and covers biological material obtained from patients [6]. The collected records are used for further investigation during medical forensic evaluations. The clinicians perform medical data diagnostic of the ”s health and reports with biological samples for further validation [7]. The forensics data includes medicine given to the patients and treatment followed by the history of patients by consulting the doctor [8]. Medical data forensics should be very secure from illegal access. Several types of algorithms such as ciphertext policies, encryption methods, and access control mechanisms, have been developed [9]. The necessary features of the body are observed, considered, and stored for accurate retrieval. The data must be secure, and only the forensics department can access the biological data samples collected [6], [10]. Medical data forensics uses the digital biological sample upgrades that lead to easy verification of the healthcare records. It is denoted as the electronic health record for accurate healthcare data monitoring [8], [10]. The electronic health record consists of several information, including patient health conditions, medical reports, drug information, and more.
The forensic health record of the patients is stored and evaluated with better retrieval without any loss. To achieve medical data forensics with better accuracy, machine learning is used [11]. By using machine learning algorithms, the data are trained and upgraded to result efficiently. There are various machine learning algorithms, such as support vector machine, neural networks, decision tree, and random forest, to improve forensic medical data [12]. It is done by evaluating the predefined knowledge by training the pursuing data [7], [11]. Thus, machine learning uses digital investigation [13], [14] for the forensic department to obtain the required crime scene. Using machine learning techniques involves achieving personalized and predictive medicine at data acquisition [15], [16]. It is also used to attain results quickly and efficiently at the time of investigation with the biological samples [17]. The proposed method addresses the security challenges in forensic medical data by introducing prediction-based data analysis [18], [19]. Transfer learning is used to obtain better accuracy in medical data forensics. This is observed in the smart meters that secure the data from unauthorized users in the smart grid. The current study’s main contributions are: (1) We increase the medical data security by applying a PDA approach. During this process, the system uses a user validating, matching, and prediction process that improves the overall data security. (2) We maximize the accuracy of medical data forensics analysis using a significant transfer-learning function. (3) Finally, we reduce data processing time while analyzing medical data. The rest of the article is organized as follows: Section 2 discusses the various researchers’ work regarding secure medical data processing; Section 3 analyzes the predictive data analysis process to examine the medical data, Section 4 evaluates the excellence of the system, and Section 5 provides a conclusion.
Section snippets
Related works
A high-security forensics service (HFS) was developed by Capuzzi et al. [20] for mental illness to take care of inpatients in prison. This work focuses on sociodemographic, criminological, and other investigation cases. HFS addresses it to a non-referred counterpart for the mental illness prisons. Li et al. [21] introduced rapid and portable forensics documentation with a saliva sample using a smartphone sensor. During this process, red emitted gold nanoclusters and silicon carbide quantum dot
PDA for smart grid healthcare systems
The smart grid is a connected network used to exchange data to the consumer in 2-way communication. Using this smart grid, communication is improved via transferring the data to the network’s relay user. The objective of this work is to improve medical data forensics by introducing a data analysis method. The data analysis model was designed to overcome intruding in medical data. Fig. 1 presents the smart grid healthcare system model.
Fig. 1 illustrates the overall general structure of the
Discussion
The performance of the proposed method was analyzed using the information in the dataset [29]. In this source, 10,000 synthetic data records of the patients are stored, verified through transmission and stored analysis. The number of users was set as 200, and a data size ranging between 10 and 100 mb was used for transmission for the accessing users. The users are provided with an independent and random access query at different time intervals. The distribution of data from a single storage
Conclusion
This paper discusses the predictive data analysis method for improving the accuracy of medical data handling in HSs. HSs powered by smart grids are considered in this analysis method to prevent illegal access to healthcare data. Access was analyzed based on the data shared to the user in different instances. Prediction for shared data from the previous instances was analyzed using transfer learning through matching and access delegations. This helped to retain the access control for accurate
CRediT authorship contribution statement
Amr Tolba: Conceptualization, Methodology, Software, Data curation, Writing - original draft. Zafer Al-Makhadmeh: Visualization, Investigation, Supervision, Software, Validation, Writing - review & editing.
Declaration of Competing Interest
The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.
Acknowledgments
This work was supported by the Deanship of Scientific Research at King Saud University [grant number RG-1439-088].
Amr Tolba received the M.Sc. and Ph.D. degrees from the Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Egypt, in 2002 and 2006, respectively. He is currently an Associate Professor with the Faculty of Science, Menoufia University, Egypt. He is on leave from Menoufia University to the Computer Science Department, Community College, King Saud University (KSU), Saudi Arabia. He has authored or coauthored over 75 scientific articles in top ranked (ISI)
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Amr Tolba received the M.Sc. and Ph.D. degrees from the Mathematics and Computer Science Department, Faculty of Science, Menoufia University, Egypt, in 2002 and 2006, respectively. He is currently an Associate Professor with the Faculty of Science, Menoufia University, Egypt. He is on leave from Menoufia University to the Computer Science Department, Community College, King Saud University (KSU), Saudi Arabia. He has authored or coauthored over 75 scientific articles in top ranked (ISI) international journals and conference proceedings. His main research interests include socially aware networks, vehicular ad hoc networks, the Internet of Things, intelligent systems, and cloud computing.
Zafer Al-Makhadmeh received the M.Sc. and Ph.D. degrees from the Department of Computer Engineering, Faculty of Information and Computer Engineering, Kharkov National Technical University of Ukraine, in 1998 and 2001, respectively. He is currently an Associate Professor with the Computer Science Department, Community College, King Saud University, Saudi Arabia. His main research interests include cloud computing, image processing, computer vision, and intelligent systems.