TARU PUBLICATIONS
 Journal of Statistics and Management Systems cover
Open Access ·Peer-reviewed·ISSN (Online): 2169-0014·ISSN (Print): 0972-0510
Powered by:Powered by

The Journal of Statistics and Management Systems (JSMS) is a world leading journal publishing high quality, rigorously peer-reviewed original research on theoretical and applied statistics and management systems since 1998. The scope is intentionally broad, but papers must make a novel contribution to the field to be considered for publication. Topics include, but are not limited to, the following: • Statistics • Applied Statistics • Industrial Statistics • Statistical Inference • Interdisciplinary role of Statistics • Actuarial Sciences • Decision Sciences • Managerial Aspects • Management Sciences • Management Information Systems

Issues up to 2022 co-published with and available at:Taylor & Francis Online
submissions@tarupublications.com
Open Access Research Article

Securing healthcare data management using machine learning and blockchain technology: A comparative performance evaluation of support vector machine and conventional classifiers

* ,

* Corresponding author · click or hover a name for details

pp. 1481–1498Vol. 26Issue 6September 2023DOI: 10.47974/JSMS-1080XML
Received:
03 Feb 2023
Published Online:
12 Sep 2023
Article type:
Research Article
Language:
EN
Article no.:
JSMS-1080
Pages:
1481–1498

Abstract

As access to healthcare has become more central to people’s daily lives, the amount of medical big data has grown exponentially. Wearable Internet of Things (IoT)-reliant technology is gaining popularity in the medical field as a means to improve patient care and reduce wait times. In recent years, billions of sensors, devices, and machines have been hooked up to the web. One such technology, remote patient monitoring, is increasingly used in modern patient care and treatment. In addition, these developments pose substantial security issues about the recording of transaction data and the transmission of information itself, and pose considerable dangers to users’ privacy. Concerns about the privacy of a patient’s medical records have the ability to discontinue treatment, putting the patient’s life in threat. Thus, a system is proposed using machine learning in combination with blockchain technology to allow secure management as well as analysis of large amounts of healthcare data. With the help of machine learning, it is feasible to sort through all of the data and extract out only the most pertinent information. This is accomplished with the help of trained methodologies. After this data has been saved, the next issue will be the exchange of data and ensuring its trustworthiness. The concept of blockchain is introduced at this point. The Blockchain technology relies on consensus to ensure that all data is accurate and that all transactions are conducted in a safe manner. The management of healthcare is one area where blockchain technology offers the ability to have a huge impact by placing patients at the center of the system and improving the privacy and portability of health records. This study is primarily concerned with finding solutions to issues relating to the administration of healthcare data by utilizing Blockchain technology and incorporating some crucial characteristics developed with Machine Learning. The performance evaluation of proposed Support Vector Machine is compared with the other conventional machine learning classifiers. It is observed from the experimental finding that performance accuracy of Support vector machine is 98% which is better as compared to other traditional machine learning classifiers.

Keywords

Subject Classifications

Primary 93A30Secondary 49K15

Acknowledgements

DG 2894

References

[1] C. Wilson, T. Hargreaves, R. Hauxwell-Baldwin, Benefits and risks of smart home technologies, Energy Policy. 103, 72-83 (2017). https://doi.org/10.1016/j.enpol.2016.12.047. 
[2] B.L. RisteskaStojkoska, K. V. Trivodaliev, A review of Internet of Things for smart home: Challenges and solutions, J. Clean. Prod. 140, 1454-1464 (2017). https://doi.org/10.1016/j.jclepro.2016.10.006. 
[3] J. ho Park, M.M. Salim, J.H. Jo, J.C.S. Sicato, S. Rathore, J.H. Park, CIoT-Net: a scalable cognitive IoT based smart city network architecture, Human-Centric Comput. Inf. Sci. 9, 1-20 (2019). https://doi.org/10.1186/s13673-019-0190-9. 
[4] M.R. Alam, M. St-Hilaire, T. Kunz, Peer-to-peer energy trading among smart homes, Appl. Energy. 238, 1434-1443 (2019). https://doi.org/10.1016/j.apenergy.2019.01.091. 
[5] P. Wang, F. Ye, X. Chen, A Smart Home Gateway Platform for Data Collection and Awareness, IEEE Commun. Mag. 56, 87-93 (2018). https://doi.org/10.1109/MCOM.2018.1701217. 
[6] J. Shen, C. Wang, T. Li, X. Chen, X. Huang, Z.H. Zhan, Secure data uploading scheme for a smart home system, Inf. Sci. (Ny). 453, 186-197 (2018). https://doi.org/10.1016/j.ins.2018.04.048. 
[7] S. Abbas, M.A. Khan, L.E. Falcon-Morales, A. Rehman, Y. Saeed, M. Zareei, A. Zeb, E.M. Mohamed, Modeling, Simulation and Optimization of Power Plant Energy Sustainability for IoT Enabled Smart Cities Empowered with Deep Extreme Learning Machine, IEEE Access. 8, 39982-39997 (2020). https://doi.org/10.1109/ACCESS.2020.2976452. 
[8] D. Nasonov, A.A. Visheratin, A. Boukhanovsky, Blockchain-based transaction integrity in distributed big data marketplace, in: Lect. Notes Comput. Sci. (Including Subser. Lect. Notes Artif. Intell. Lect. Notes Bioinformatics) (2018). https://doi.org/10.1007/978-3-319-93698-7_43. 
[9] R.A. Michelin, A. Dorri, M. Steger, R.C. Lunardi, S.S. Kanhere, R. Jurdak, A.F. Zorzo, SpeedyChain: A framework for decoupling data from blockchain for smart cities, in: ACM Int. Conf. Proceeding Ser., (2018). https://doi.org/10.1145/3286978.3287019. 
[10] B. Xiong, K. Yang, J. Zhao, K. Li, Robust dynamic network traffic partitioning against malicious attacks, J. Netw. Comput. Appl. 87, 20-31 (2017). https://doi.org/10.1016/j.jnca.2016.04.013. 
[11] C. Yin, J. Xi, R. Sun, J. Wang, Location privacy protection based on differential privacy strategy for big data in industrial internet of things, IEEE Trans. Ind. Informatics. 14, 3628-3636 (2018). https://doi.org/10.1109/TII.2017.2773646.
[12] S. Aggarwal, R. Chaudhary, G.S. Aujla, N. Kumar, K.K.R. Choo, A.Y. Zomaya, Blockchain for smart communities: Applications, challenges and opportunities, J. Netw. Comput. Appl. 144, 13-48 (2019). https://doi.org/10.1016/j.jnca.2019.06.018. 
[13] M. Andoni, V. Robu, D. Flynn, S. Abram, D. Geach, D. Jenkins, P. McCallum, A. Peacock, Blockchain technology in the energy sector: A systematic review of challenges and opportunities, Renew. Sustain. Energy Rev. 100, 143-174 (2019). https://doi.org/10.1016/j.rser.2018.10.014. 
[14] G. Li, M. Dong, L.T. Yang, K. Ota, J. Wu, J. Li, Preserving Edge Knowledge Sharing among IoT Services: A Blockchain-Based Approach, IEEE Trans. Emerg. Top. Comput. Intell. 4, 653-665 (2020). https://doi.org/10.1109/TETCI.2019.2952587. 
[15] Z. Zhou, B. Wang, M. Dong, K. Ota, Secure and Efficient Vehicle-to-Grid Energy Trading in Cyber Physical Systems: Integration of Blockchain and Edge Computing, IEEE Trans. Syst. Man, Cybern. Syst. 50, 43-57 (2020). https://doi.org/10.1109/TSMC.2019.2896323. 
[16] X. Du, B. Chen, M. Ma, Y. Zhang, Research on the Application of Blockchain in Smart Healthcare: Constructing a Hierarchical Framework, J. Healthc. Eng. (2021). https://doi.org/10.1155/2021/6698122. 
[17] B. Ihnaini, M.A. Khan, T.A. Khan, S. Abbas, M.S. Daoud, M. Ahmad, M.A. Khan, A Smart Healthcare Recommendation System for Multidisciplinary Diabetes Patients with Data Fusion Based on Deep Ensemble Learning, Comput. Intell. Neurosci. (2021). https://doi.org/10.1155/2021/4243700. 
[18] Naregal K, Kalmani V. Study of lightweight ABE for cloud based IoT. In 2020 Fourth International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud)(I-SMAC), pp. 134-137. IEEE (2020 Oct 7).
[19] Sonwalkar PK, Kalmani V. Energy Efficient Hop-by-Hop Retransmission and Congestion Mitigation of an Optimum Routing and Clustering Protocol for WSNs. International Journal of Advanced Computer Science and Applications. 13(3) (2022).
[20] Kalmani VH, Alias SS, Jogadand RM, Rai HM. An Efficient Key Management Scheme with Key Agreement to Mitigate Malicious Attacks for Wireless Mesh Network. International Journal of Computer Applications. 56(6).1; 56(6) (2012 Jan 1).
[21] Nanda R, Sharma A, Choraria P, Pareek A, Tiwari N, Jain A. Statistical analysis of query processing time in cache-based cloud database systems. Journal of Statistics and Management Systems. 25(7) : 1673-83 (2022 Oct 3).
[22] Bhatia S, Sachdeva S, Goswami P. Air pollution prediction and hotspot detection using machine learning. Journal of Statistics and Management Systems. 25(7) : 1553-64 (2022 Oct 3).
[23] Rout AK, Sethy A, Nayak SR. Adaptive MLELM-AE model for efficient prediction of stock market data. Journal of Statistics and Management Systems. 25(7) : 1541-52 (2022 Oct 3).
[24] Soni Y, Gandhi GC, Goyal D. A secure e-health framework for rural Rajasthan. Journal of Statistics and Management Systems. 1-0 (2022 Sep 10).
[25] Sinem Şahnagil, H. Salahaddin Gezici, Mustafa Kocaoğlu. Evaluation of digital transformation process through the Presidential Government System: Digital transformation office. Journal of Information and Optimization Sciences. Volume 43, 2022 - Issue 7: 7th International Conference on Embracing Trasformation: Innovation and Creation Hybrid (May 26-28, 2022).
[26] Rita Roy, Kavitha Chekuri, G. Sandhya, Surya Kant Pal. Exploring the blockchain for sustainable food supply chain. Journal of Information and Optimization Sciences. Volume 43, 2022 - Issue 7: 7th International Conference on Embracing Trasformation: Innovation and Creation Hybrid (May 26-28, 2022).

Views: 214Downloads: 5Citations: 0