FUZZY FUSION: REVOLUTIONIZING SMART HEALTHCARE MONITORING WITH BLOCKCHAIN INTEGRATION

Main Article Content

Amjad Hussain
Jamshaid Iqbal Janjua
Shahbaz Saeed
Kashif Jamshaid
Ahmad Rafi Shahid
Tahir Abbas
Umer Farooq

Keywords

Blockchain, healthcare, IoMT, framework, measures

Abstract

Blockchain and the Internet of Medical Things (IoMT) are widely used in numerous fields, including Healthcare, for applications like secure storage, transactions, in addition development automation. There are no security measures for IoMT devices, which can easily be hacked or affected. Providing remote patient diagnosis is another requirement of smart healthcare. Data protection, costs, memory, scalability, trust, and openness among diverse platforms are all major concerns for the smart healthcare framework. Moreover, blockchain is a revolutionary innovation by immutability structures that provide secure administration, authentication, and access control for IoMT devices. IoMT devices support immutability, as well as secure management provided by blockchain technology. Remote data processing and collection are key features of the IoMT service, a cloud-based internet application. To meet the needs of the healthcare area, an accessible, fault-tolerant, secure, perceptible, and private blockchain is required. In this research work, a blockchain-based autonomous model is being proposed by utilizing fused machine learning to enhance the quality of patient healthcare monitoring in a better and more efficient way. The proposed framework simulation results are enhanced than the previously published approaches in terms of 93% accuracy as well as a 7% miss rate.

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References

1. E. Jamoom, N. Yang and E. Hing, ‘‘Adoption of certified electronic health record systems and electronic information sharing in physician offices: United States, 2013 and 2014,’’ U.S. Dept. Health Hum. Services, Centers Disease Control Prevention, Nat. Center Health Statist. Hyattsville, vol. 236, 2016.
2. A. Bahga and V. K. Madisetti, ‘‘A cloud-based approach for interoperable electronic health records,’’ IEEE J. Biomed. Health Inform., vol. 17, no. 5, pp. 894–906, 2013.
3. T. M. Ghazal, M. Anam, M. K. Hasan, M. Hussain, M.S. Farooq et al., “Hep-pred: hepatitis c staging prediction using fine gaussiansvm,” Computers, Materials & Continua, vol. 69, no. 1, pp.191-203, 2021.
4. T. M. Ghazal, T. M. K. Hasan, M. T. Alshurideh, H. M. Alzoubi, M. Ahmad et al., “Iot for smart cities: Machine learning approaches in smart healthcare—A review,” Future Internet, vol. 13, no. 8, pp.218, 2021.
5. C. Moore, M. O. Neill, E. Sullivan, Y. Doröz and B. Sunar, ‘‘Practical homomorphic encryption: A survey,’’ IEEE Explore, vol. 23, pp. 2792–2795, 2014.
6. D. Tapscott and A. Tapscott, “Blockchain revolution: how the technology behind bitcoin is changing money,” Business, and the World, vol. 6, no. 4, pp. 1-14, 2016.
7. S. Haber and W. S. Stornetta, ‘‘How to timestamp a digital document,’’ In Conference on the Theory and Application of Cryptography, Springer, Berlin, Heidelberg, pp. 437-455, 2013.
8. D. Bayer, S. Haber and W. S. Stornetta, ‘‘Improving the efficiency and reliability of digital timestamping,’’ In Sequences Ii, Springer, New York, NY, pp. 329-334, 2013.
9. J. Wu, Y. Feng and P. Sun, “Sensor fusion for recognition of activities of daily living,” Sensors, vol. 18, no. 3, pp. 4-29, 2018.
10. Y. Chen, S. Ding, Z. Xu, H. Zheng and S. Yang, “Blockchain-based medical records secure storage and medical service framework,” Journal of medical systems, vol. 43, no. 1, pp. 1-9, 2019.
11. K. Fan, S. Wang, Y. Ren, H. Li and Y. Yang, “Medblock: efficient and secure medical data sharing via blockchain,” J. Med. Syst, vol. 42, pp. 136, 2018.
12. H. Li, L. Zhu, M. Shen, F. Gao, X. Tao et al., “Blockchain-based data preservation system for medical data,” J. Med. Syst., vol. 42, no. 3, pp. 141, 2018.
13. A. Zhang and X. Lin, “Towards secure and privacy-preserving data sharing in e-health systems via consortium blockchain,” J. Med. Syst., vol. 42, no. 4, pp. 140, 2018.
14. H. D. Zubaydi, Y. W. Chong, K. Ko, S. M. Hanshi and S. Karuppayah, “A review on the role of blockchain technology in the healthcare domain,” Electronics, vol. 8, no. 5, pp. 679, 2019.
15. M. K. Hasan, T. M. Ghazal, A. Alkhalifah, A., K. A. A. Bakar, A. Omidvar et al., “Fischer linear discrimination and quadratic discrimination analysis–based data mining technique for internet of things framework for Healthcare,” Frontiers in public health, vol. 9, no. 1, pp. 1-12, 2021.
16. T. Batool, S. Abbas, Y. Alhwaiti, M. Saleem, M. Ahmad et al., “Intelligent model of ecosystem for smart cities using artificial neural networks,” Intelligent Automation and Soft Computing, vol. 30, no. 2, pp. 513-525, 2021.
17. T. M. Ghazal, S. Abbas, S. Munir, M. A. Khan, M. Ahmad et al., “Alzheimer disease detection empowered with transfer learning,” Computers, Materials & Continua, vol. 70, no. 3, pp. 5005-5019, 2022.
18. Khan, T. A., Fatima, A., Shahzad, T., Alissa, K., Ghazal, T. M., Al-Sakhnini, M. M., ...& Ahmed, A. (2023). Secure IoMT for disease prediction empowered with transfer learning in healthcare 5.0, the concept and case study. IEEE Access, 11, 39418-39430.
19. B. Ihnaini, M. A. Khan, T. A. Khan, S. Abbas, M. S. Daoud et al., “A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning,” Computational Intelligence and Neuroscience, vol. 12, no. 2, pp. 1-13, 2021.
20. Ihnaini, B., Khan, M. A., Khan, T. A., Abbas, S., Daoud, M. S., Ahmad, M., & Khan, M. A. (2021). A smart healthcare recommendation system for multidisciplinary diabetes patients with data fusion based on deep ensemble learning. Computational Intelligence and Neuroscience, 2021.
21. A. H. Khan, M. A. Khan, S. Abbas, S. Y. Siddiqui, M. A. Saeed et al., "Simulation, modeling, and optimization of intelligent kidney disease predication empowered with computational intelligence approaches," Computers, Materials & Continua, vol. 67, no.2, pp. 1399–1412, 2021.
22. A. Anand, and A.K. Singh, “Cloud based secure watermarking using IWT-Schur-RSVD with fuzzy inference system for smart healthcare applications,” Sustainable Cities and Society, 75, p.103398, 2021.
23. Khan, T. A., Abbas, S., Ditta, A., Khan, M. A., Alquhayz, H., Fatima, A., & Khan, M. F. (2020). IoMT-Based Smart Monitoring Hierarchical Fuzzy Inference System for Diagnosis of COVID-19. Computers, Materials & Continua, 65(3).
24. Turnip, A., Rizqywan, M.I., Kusumandari, D.E., Turnip, M. and Sihombing, P., 2018, March. Classification of ECG signal with support vector machine method for arrhythmia detection. In Journal of Physics: Conference Series (Vol. 970, No. 1, p. 012012). IOP Publishing.
25. M. Aslam, “Removal of the noise & blurriness using global & local image enhancement equalization techniques,” International Journal of Computational and Innovative Sciences, vol. 1, no. 1, pp-1-18, 2022.
26. S.Muneer, M.A. Rasool, “AA systematic review: Explainable Artificial Intelligence (XAI) based disease prediction,’ International Journal of Advanced Sciences and Computing, vol. 1, no. 1, pp.1-6.2022.
27. U. Ullah, “Intelligent intrusion detection system for apache web server empowered with machine learning approaches”, International Journal of Computational and Innovative Sciences, vol. 1, no. 1, pp. 2022.
28. Sathya, D., Sudha, V. and Jagadeesan, D., 2020. Application of machine learning techniques in healthcare. In Handbook of Research on Applications and Implementations of Machine Learning Techniques (pp. 289-304). IGI Global.
29. M. Asif, S. Abbas, M. A. Khan, A. Fatima, M. A. Khan et al., "MapReduce based intelligent model for intrusion detection using machine learning technique." Journal of King Saud University-Computer and Information Sciences, vol. inpress, 2021.
30. T. M. Ghazal, A. U. Rehman, M. Saleem, M. Ahmad, S. Ahmad et al., “Intelligent model to predict early liver disease using machine learning technique,” International Conference on Business Analytics for Technology and Security, Karachi, Pakistan, pp. 1-5, 2019.
31. A. Masood, B. Sheng, P. Li, X. Hou, X. Wei et al.,”Computer-assisted decision support system in pulmonary cancer detection and stage classification on ct images,” Journal of Biomedical Informatics, vol. 79, pp. 117-128, 2018.
32. W. Shen, M. Zhou, F. Yang, D. Yu, D. Dong et al., “Multi-crop convolutional neural networks for lung nodule malignancy suspiciousness classification,” Pattern Recognition, vol. 61, no. 4, pp. 663-673, 2017.
33. Waring, J., Lindvall, C. and Umeton, R., 2020. Automated machine learning: Review of the state-of-the-art and opportunities for healthcare. Artificial intelligence in medicine, 104, p.101822.
34. Al-Dmour, N.A., Salahat, M., Nair, H.K., Kanwal, N., Saleem, M. and Aziz, N., 2022, October. Intelligence Skin Cancer Detection using IoT with a Fuzzy Expert System. In 2022 International Conference on Cyber Resilience (ICCR) (pp. 1-6). IEEE.
35. Atta, A., Khan, M.A., Asif, M., Issa, G.F., Said, R.A. and Faiz, T., 2022, October. Classification of Skin Cancer empowered with convolutional neural network. In 2022 International Conference on Cyber Resilience (ICCR) (pp. 01-06). IEEE.
36. Asif, M., Khan, T.A., Taleb, N., Said, R.A., Siddiqui, S.Y. and Batool, G., 2022, February. A Proposed Architecture for Traffic Monitoring & Control System via LiFi Technology in Smart Homes. In 2022 International Conference on Business Analytics for Technology and Security (ICBATS) (pp. 1-3). IEEE.

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