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A Review on Machine Learning and Blockchain Technology in E-Healthcare

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Intelligent Systems Design and Applications (ISDA 2022)

Part of the book series: Lecture Notes in Networks and Systems ((LNNS,volume 717))

Abstract

Healthcare always plays an important role in human life. All stakeholders, including physicians, nurses, patients, life insurance agents, etc., may easily access patients’ medical information, credit goes to cloud computing, which is a key factor in this transformation. Cloud services provide flexible, affordable, and wide-ranging mobile access to patients’ Electronic Health Records (EHR). Despite the immense advantages of the cloud, Patients’ EHR security and privacy are key concerns, like real-time data access. These EHR data can be useful in finding and diagnosing chronic diseases like cancer, heart attack, diabetes, etc. Due to the severity of these diseases, most of the population is dying because of the lack of prediction techniques in the early stages of diseases. Hence, it becomes one of the most promising research problems to analyze and find solutions to overcome this loss. This work includes a review of e-healthcare-related research studies that can aid researchers in understanding the drawbacks and benefits of current healthcare systems that use machine learning, blockchain technology, and other components to ensure privacy and security.

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References

  1. Meskó, B., Drobni, Z., Bényei, É., Gergely, B., Győrffy, Z.: Digital health is a cultural transformation of traditional healthcare. Mhealth 3 (2017)

    Google Scholar 

  2. Sahi, M.A., et al.: Privacy preservation in e-healthcare environments: state of the art and future directions. IEEE Access. 30(6), 464–478 (2017)

    Google Scholar 

  3. Munirathinam, T., Ganapathy, S., Kannan, A.: Cloud and IoT based privacy preserved e-Healthcare system using secured storage algorithm and deep learning. J. Intell. Fuzzy Syst. 39(3), 3011–3023 (2020)

    Article  Google Scholar 

  4. Mustafa, M., Alshare, M., Bhargava, D., Neware, R., Singh, B., Ngulube, P.: Perceived security risk based on moderating factors for blockchain technology applications in cloud storage to achieve secure healthcare systems. Comput. Math. Methods Med. 19, 2022 (2022)

    Google Scholar 

  5. Dhillon, A., Singh, A.: Machine learning in healthcare data analysis: a survey. J. Biol. Today’s World. 8(6), 1 (2019)

    Google Scholar 

  6. Alanazi, A.: Using machine learning for healthcare challenges and opportunities. Inf. Med. Unlocked. 21, 100924 (2022)

    Article  Google Scholar 

  7. Tumpa, E.S., Dey, K.: A review on applications of machine learning in healthcare. In: 2022 6th International Conference on Trends in Electronics and Informatics (ICOEI), 28 April 2022, pp. 1388–1392. IEEE (2022)

    Google Scholar 

  8. Ferdous, M., Debnath, J., Chakraborty, N.R.: Machine learning algorithms in healthcare: a literature survey. In: 2020 11th International conference on computing, communication and networking technologies (ICCCNT), 1 July 2020, pp. 1–6. IEEE (2020)

    Google Scholar 

  9. Hossain, M.A., Ferdousi, R., Alhamid, M.F.: Knowledge-driven machine learning-based framework for early-stage disease risk prediction in edge environment. J. Para. Distrib. Comput. 1(146), 25–34 (2020)

    Article  Google Scholar 

  10. Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access. 19(7), 81542–81554 (2019)

    Article  Google Scholar 

  11. Soudan, B., Dandachi, F.F., Nassif, A.B.: Attempting cardiac arrest prediction using artificial intelligence on vital signs from Electronic Health Records. Smart Health. 23, 100294 (2022)

    Article  Google Scholar 

  12. Guo, C., Tian, P., Choo, K.K.: Enabling privacy-assured fog-based data aggregation in E-healthcare systems. IEEE Trans. Ind. Inf. 17(3), 1948–1957 (2020)

    Article  Google Scholar 

  13. Singh, S., Rathore, S., Alfarraj, O., Tolba, A., Yoon, B.: A framework for privacy-preservation of IoT healthcare data using Federated Learning and blockchain technology. Futur. Gener. Comput. Syst. 1(129), 380–388 (2022)

    Article  Google Scholar 

  14. Li, J.P., Haq, A.U., Din, S.U., Khan, J., Khan, A., Saboor, A.: Heart disease identification method using machine learning classification in e-healthcare. IEEE Access. 9(8), 107562–107582 (2020)

    Article  Google Scholar 

  15. Balusamy, B., Chilamkurti, N., Beena, L.A., Poongodi, T.: Blockchain and machine learning for e-healthcare systems. In: Blockchain and Machine Learning for e-Healthcare Systems, pp. 1–481 (2021)

    Google Scholar 

  16. Amanat, A., Rizwan, M., Maple, C., Zikria, Y.B., Almadhor, A.S., Kim, S.W.: Blockchain and cloud computing-based secure electronic healthcare records storage and sharing. Front. Public Health 19, 2309 (2022)

    Google Scholar 

  17. Tandon, A., Dhir, A., Islam, A.N., Mäntymäki, M.: Blockchain in healthcare: a systematic literature review, synthesizing framework and future research agenda. Comput. Ind. 1(122), 103290 (2020)

    Article  Google Scholar 

  18. Javed, W., Aabid, F., Danish, M., Tahir, H., Zainab, R.: Role of blockchain technology in healthcare: a systematic review. In: 2021 International Conference on Innovative Computing (ICIC), 9 Nov 2021, pp. 1–8. IEEE (2021)

    Google Scholar 

  19. Taloba, A.I., Rayan, A., Elhadad, A., Abozeid, A., Shahin, O.R., Abd El-Aziz, R.M.: A framework for secure healthcare data management using blockchain technology. Int. J. Adv. Comput. Sci. Appl. 12(12) (2021)

    Google Scholar 

  20. Khezr, S., Moniruzzaman, M., Yassine, A., Benlamri, R.: Blockchain technology in healthcare: a comprehensive review and directions for future research. Appl. Sci. 9(9), 1736 (2019)

    Article  Google Scholar 

  21. Sanober, A., Anwar, S.: Blockchain for content protection in E-healthcare: a case study for COVID-19. In: 2022 8th International Conference on Advanced Computing and Communication Systems (ICACCS), 5 Mar 2022, vol. 1, pp. 661–666. IEEE (2022)

    Google Scholar 

  22. Shaikh, Z.A., Khan, A.A., Teng, L., Wagan, A.A., Laghari, A.A.: BIoMT modular infrastructure: the recent challenges, issues, and limitations in blockchain hyperledger-enabled e-healthcare application. Wirel. Commun. Mobile Comput. (2022)

    Google Scholar 

  23. Wilcox, A.B., Gallagher, K.D., Boden-Albala, B., Bakken, S.R.: Research data collection methods: from paper to tablet computers. Med. Care 1, S68-73 (2012)

    Article  Google Scholar 

  24. Qureshi, M.M., Farooq, A., Qureshi, M.M.: Current eHealth Challenges and recent trends in eHealth applications. arXiv preprint arXiv:2103.01756 (2021)

  25. Bordoloi, D., Singh, V., Sanober, S., Buhari, S.M., Ujjan, J.A., Boddu, R.: Deep learning in healthcare system for quality of service. J. Healthcare Eng. 8, 2022 (2022)

    Google Scholar 

  26. Geweid, G.G., Abdallah, M.A.: A new automatic identification method of heart failure using improved support vector machine based on duality optimization technique. IEEE Access. 4(7), 149595–149611 (2019)

    Article  Google Scholar 

  27. Liu, X., et al.: A hybrid classification system for heart disease diagnosis based on the RFRS method. Comput. Math. Methods Med. 3, 2017 (2017)

    MathSciNet  Google Scholar 

  28. Sadad, T., Bukhari, S.A., Munir, A., Ghani, A., El-Sherbeeny, A.M., Rauf, H.T.: Detection of cardiovascular disease based on PPG signals using machine learning with cloud computing. Comput. Intell. Neurosci. 4, 2022 (2022)

    Google Scholar 

  29. Kumari, V., Reddy, P.B., Sudhakar, C.: Performance interpretation of machine learning based classifiers for e-healthcare system in fog computing network. In: 2022 IEEE Students Conference on Engineering and Systems (SCES), 1 July 2022, pp. 01–05. IEEE (2022)

    Google Scholar 

  30. Haq, A.U., et al.: Intelligent machine learning approach for effective recognition of diabetes in E-healthcare using clinical data. Sensors 20(9), 2649 (2020)

    Article  Google Scholar 

  31. Mishra, S., Thakkar, H.K., Singh, P., Sharma, G.: A decisive metaheuristic attribute selector enabled combined unsupervised-supervised model for chronic disease risk assessment. Comput. Intell. Neurosci. 8, 2022 (2022)

    Google Scholar 

  32. Pal, S.: Chronic kidney disease prediction using machine learning techniques. Biomed. Mater. Dev. 31, 1–7 (2022)

    Google Scholar 

  33. Ramzan, S., Aqdus, A., Ravi, V., Koundal, D., Amin, R., Al Ghamdi, M.A.: Healthcare applications using blockchain technology: motivations and challenges. IEEE Trans. Eng. Manag. (2022)

    Google Scholar 

  34. Singh, K.K., Elhoseny, M., Singh, A., Elngar, A.A. (eds.): Machine Learning and the Internet of Medical Things in Healthcare. Academic Press, Cambridge (2021)

    Google Scholar 

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Author information

Authors and Affiliations

  1. SCOPE, VIT, Vellore, 632014, Tamil Nadu, India

    Deepika Tenepalli & Navamani Thandava Meganathan

Authors
  1. Deepika Tenepalli
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  2. Navamani Thandava Meganathan
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Corresponding author

Correspondence to Navamani Thandava Meganathan .

Editor information

Editors and Affiliations

  1. Faculty of Computing and Data Science, FLAME University, Pune, Maharashtra, India

    Ajith Abraham

  2. Center for Smart Computing Continuum, Burgenland, Austria

    Sabri Pllana

  3. University of Bari, Bari, Italy

    Gabriella Casalino

  4. University of Jinan, Jinan, Shandong, China

    Kun Ma

  5. Department of Computer Science and Engineering, Thapar Institute of Engineering and Technology, Patiala, Punjab, India

    Anu Bajaj

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Tenepalli, D., Thandava Meganathan, N. (2023). A Review on Machine Learning and Blockchain Technology in E-Healthcare. In: Abraham, A., Pllana, S., Casalino, G., Ma, K., Bajaj, A. (eds) Intelligent Systems Design and Applications. ISDA 2022. Lecture Notes in Networks and Systems, vol 717. Springer, Cham. https://doi.org/10.1007/978-3-031-35510-3_33

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