Skip to main content
SpringerLink
Log in

Federated Edge-Cloud Framework for Heart Disease Risk Prediction Using Blockchain

  • Conference paper
  • First Online:
Internet of Things. Advances in Information and Communication Technology (IFIPIoT 2023)

Part of the book series: IFIP Advances in Information and Communication Technology ((IFIPAICT,volume 684))

Included in the following conference series:

Abstract

Heart disease is a term used to describe a range of conditions that affect the heart, such as coronary artery disease, heart failure, and arrhythmias. It is a leading cause of death globally and can be prevented or managed through lifestyle changes and medical treatments, such as medication and surgery. The use of federated learning and edge computing has become increasingly popular for machine learning tasks, especially in the healthcare domain. However, privacy and security concerns remain major challenges in the adoption of such technologies. In this article, we propose a blockchain-enabled federated edge-cloud framework for heart disease risk prediction to address these challenges. The proposed framework involves the use of blockchain to secure the data sharing and model aggregation process, while edge devices are utilized for data preprocessing and feature extraction, and cloud servers are used for model training and validation. The federated learning approach ensures data privacy, while the use of blockchain provides immutability, transparency, and accountability to the system.

This is a preview of subscription content, log in via an institution to check access.

Access this chapter

Log in via an institution
Chapter
USD 29.95
Price excludes VAT (USA)
  • Available as PDF
  • Read on any device
  • Instant download
  • Own it forever
eBook
USD 84.99
Price excludes VAT (USA)
  • Available as EPUB and PDF
  • Read on any device
  • Instant download
  • Own it forever
Hardcover Book
USD 109.99
Price excludes VAT (USA)
  • Durable hardcover edition
  • Dispatched in 3 to 5 business days
  • Free shipping worldwide - see info

Tax calculation will be finalised at checkout

Purchases are for personal use only

Institutional subscriptions

References

  1. Abbas, S., et al.: Fused weighted federated deep extreme machine learning based on intelligent lung cancer disease prediction model for healthcare 5.0. Int. J. Intell. Syst. 2023, Article ID 2599161, 14 (2023). https://doi.org/10.1155/2023/2599161

  2. Appiah, D., Capistrant, B.D.: Cardiovascular disease risk assessment in the united states and low-and middle-income countries using predicted heart/vascular age. Sci. Reports 7(1), 16673 (2017)

    Google Scholar 

  3. Azevedo, T.d.A., Moreira, M.L.V., Nucera, A.P.C.d.S.: Cardiovascular risk estimation by the ASCVD risk estimator application in a university hospital. Int. J. Cardiovasc. Sci. 31, 492–498 (2018)

    Google Scholar 

  4. Ballinger, B., et al.: Deepheart: semi-supervised sequence learning for cardiovascular risk prediction. In: Proceedings of the AAAI conference on artificial intelligenc, vol. 32 (2018)

    Google Scholar 

  5. Cascino, T.M., et al.: Racial inequities in access to ventricular assist device and transplant persist after consideration for preferences for care: A report from the revival study. Circ.: Heart Failure 16(1), e009745 (2023)

    Google Scholar 

  6. Çaydaş, U., Hasçalık, A., Ekici, S.: An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM. Expert Syst. Appl. 36(3), 6135–6139 (2009)

    Article  Google Scholar 

  7. Chatterjee, P., Das, D., Rawat, D.B.: Use of federated learning and blockchain towards securing financial services. arXiv preprint arXiv:2303.12944 (2023)

  8. Chenthara, S., Ahmed, K., Wang, H., Whittaker, F.: Security and privacy-preserving challenges of e-health solutions in cloud computing. IEEE Access 7, 74361–74382 (2019). https://doi.org/10.1109/ACCESS.2019.2919982

    Article  Google Scholar 

  9. Das, D., Banerjee, S., Dasgupta, K., Chatterjee, P., Ghosh, U., Biswas, U.: Blockchain enabled SDN framework for security management in 5g applications. In: 24th International Conference on Distributed Computing and Networking, pp. 414–419 (2023)

    Google Scholar 

  10. Das, D., Banerjee, S., Mansoor, W., Biswas, U., Chatterjee, P., Ghosh, U.: Design of a secure blockchain-based smart iov architecture. In: 2020 3rd International Conference on Signal Processing and Information Security (ICSPIS), pp. 1–4. IEEE (2020)

    Google Scholar 

  11. Dash, S., Tripathy, R.K., Dash, D.K., Panda, G., Pachori, R.B.: Multiscale domain gradient boosting models for the automated recognition of imagined vowels using multichannel eeg signals. IEEE Sensors Lett. 6(11), 1–4 (2022)

    Article  Google Scholar 

  12. DeBoever, C., Tanigawa, Y., Aguirre, M., McInnes, G., Lavertu, A., Rivas, M.A.: Assessing digital phenotyping to enhance genetic studies of human diseases. Am. J. Human Genetics 106(5), 611–622 (2020)

    Article  Google Scholar 

  13. Djenouri, Y., Belhadi, A., Srivastava, G., Ghosh, U., Chatterjee, P., Lin, J.C.W.: Fast and accurate deep learning framework for secure fault diagnosis in the industrial internet of things. IEEE Internet of Things J. (2021)

    Google Scholar 

  14. Franco, R.Z., Fallaize, R., Lovegrove, J.A., Hwang, F.: Popular nutrition-related mobile apps: a feature assessment. JMIR mHealth and uHealth 4(3), e5846 (2016)

    Article  Google Scholar 

  15. Guo, A., Pasque, M., Loh, F., Mann, D.L., Payne, P.R.: Heart failure diagnosis, readmission, and mortality prediction using machine learning and artificial intelligence models. Curr. Epidemiol. Reports 7, 212–219 (2020)

    Article  Google Scholar 

  16. Hasanova, H., Tufail, M., Baek, U.J., Park, J.T., Kim, M.S.: A novel blockchain-enabled heart disease prediction mechanism using machine learning. Comput. Electr. Eng. 101, 108086 (2022). https://doi.org/10.1016/j.compeleceng.2022.108086, https://www.sciencedirect.com/science/article/pii/S004579062200341X

  17. Hemann, B.A., Bimson, W.F., Taylor, A.J.: The framingham risk score: an appraisal of its benefits and limitations. Am. Heart Hospital J. 5(2), 91–96 (2007)

    Article  Google Scholar 

  18. Iqbal, S.M., Mahgoub, I., Du, E., Leavitt, M.A., Asghar, W.: Advances in healthcare wearable devices. NPJ Flexible Electron. 5(1), 9 (2021)

    Article  Google Scholar 

  19. Khan, M.A., Algarni, F.: A healthcare monitoring system for the diagnosis of heart disease in the IoMT cloud environment using MSSO-ANFIS. IEEE Access 8, 122259–122269 (2020). https://doi.org/10.1109/ACCESS.2020.3006424

    Article  Google Scholar 

  20. Livingstone, S., et al.: Effect of competing mortality risks on predictive performance of the qrisk3 cardiovascular risk prediction tool in older people and those with comorbidity: external validation population cohort study. Lancet Healthy Longevity 2(6), e352–e361 (2021)

    Article  Google Scholar 

  21. Mayeux, R.: Biomarkers: potential uses and limitations. NeuroRx 1, 182–188 (2004)

    Article  Google Scholar 

  22. Mohan, S., Thirumalai, C., Srivastava, G.: Effective heart disease prediction using hybrid machine learning techniques. IEEE Access 7, 81542–81554 (2019). https://doi.org/10.1109/ACCESS.2019.2923707

    Article  Google Scholar 

  23. Mortazavi, B.J., et al.: Analysis of machine learning techniques for heart failure readmissions. Circ.: Cardiovasc. Qual. Outcomes 9(6), 629–640 (2016)

    Google Scholar 

  24. Muthu, B., et al.: A framework for extractive text summarization based on deep learning modified neural network classifier. Trans. Asian Low-Resource Lang. Inform. Process. 20(3), 1–20 (2021)

    Article  Google Scholar 

  25. Nagesh, S.H., Kumar, K.R.A., Rajgopal, K.T.: Cloud architectures encountering data security and privacy concerns - a review. In: 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS), pp. 1729–1735 (2017). https://doi.org/10.1109/ICECDS.2017.8389745

  26. Nashif, S., Raihan, M.R., Islam, M.R., Imam, M.H.: Heart disease detection by using machine learning algorithms and a real-time cardiovascular health monitoring system. World J. Eng. Technol. 6(4), 854–873 (2018)

    Article  Google Scholar 

  27. Nowbar, A.N., Gitto, M., Howard, J.P., Francis, D.P., Al-Lamee, R.: Mortality from ischemic heart disease: Analysis of data from the world health organization and coronary artery disease risk factors from NCD risk factor collaboration. Circul.: Cardiovasc. Qual. Outcomes 12(6), e005375 (2019)

    Google Scholar 

  28. Pletcher, M.J., Tice, J.A., Pignone, M., Browner, W.S.: Using the coronary artery calcium score to predict coronary heart disease events: a systematic review and meta-analysis. Arch. Internal Med. 164(12), 1285–1292 (2004)

    Article  Google Scholar 

  29. Reddy, D.K.K., Nayak, J., Naik, B., Ghosh, U., Sharma, P.K.: Exact greedy algorithm based split finding approach for intrusion detection in fog-enabled IoT environment. J. Inform. Secur. Appl. 60, 102866 (2021)

    Google Scholar 

  30. Sarmah, S.S.: An efficient IoT-based patient monitoring and heart disease prediction system using deep learning modified neural network. IEEE Access 8, 135784–135797 (2020). https://doi.org/10.1109/ACCESS.2020.3007561

    Article  Google Scholar 

  31. Saw, M., Saxena, T., Kaithwas, S., Yadav, R., Lal, N.: Estimation of prediction for getting heart disease using logistic regression model of machine learning. In: 2020 International Conference on Computer Communication and Informatics (ICCCI), pp. 1–6 (2020). https://doi.org/10.1109/ICCCI48352.2020.9104210

  32. Simpson, C.C., Mazzeo, S.E.: Calorie counting and fitness tracking technology: Associations with eating disorder symptomatology. Eating Behav. 26, 89–92 (2017)

    Article  Google Scholar 

  33. Siva Shankar, G., Ashokkumar, P., Vinayakumar, R., Ghosh, U., Mansoor, W., Alnumay, W.S.: An embedded-based weighted feature selection algorithm for classifying web document. Wirel. Commun. Mobile Comput. 2020, 1–10 (2020)

    Article  Google Scholar 

  34. Udler, M.S., McCarthy, M.I., Florez, J.C., Mahajan, A.: Genetic risk scores for diabetes diagnosis and precision medicine. Endocrine Rev. 40(6), 1500–1520 (2019)

    Article  Google Scholar 

  35. Virani, S.S., et al.: Heart disease and stroke statistics-2020 update: a report from the American heart association. Circ.: Heart Failure 141(9), e139–e596 (2020)

    Google Scholar 

  36. Yaqoob, M.M., Nazir, M., Khan, M.A., Qureshi, S., Al-Rasheed, A.: Hybrid classifier-based federated learning in health service providers for cardiovascular disease prediction. Appl. Sci. 13(3) (2023). https://doi.org/10.3390/app13031911, https://www.mdpi.com/2076-3417/13/3/1911

  37. Yuan, X., Chen, J., Zhang, K., Wu, Y., Yang, T.: A stable AI-based binary and multiple class heart disease prediction model for IoMT. IEEE Trans. Indust. Inform. 18(3), 2032–2040 (2022). https://doi.org/10.1109/TII.2021.3098306

    Article  Google Scholar 

  38. Zeng, Z., Deng, Y., Li, X., Naumann, T., Luo, Y.: Natural language processing for EHR-based computational phenotyping. IEEE/ACM Trans. Comput. Biol. Bioinform. 16(1), 139–153 (2018)

    Article  Google Scholar 

Download references

Acknowledgement

This work was supported by a 2023-2024 Faculty Research Initiative grant from the Thurgood Marshall College Fund and the Novartis US Foundation.

Author information

Authors and Affiliations

  1. Department of CS and DS, Meharry Medical College, Nashville, TN, USA

    Uttam Ghosh & Nadine Shillingford

  2. Department of CSE, Narula Institute of Technology, Kolkata, West Bengal, India

    Debashis Das

  3. Department of EE and CS, Howard University, Washington, DC, USA

    Pushpita Chatterjee

Authors
  1. Uttam Ghosh
    View author publications

    You can also search for this author in PubMed Google Scholar

  2. Debashis Das
    View author publications

    You can also search for this author in PubMed Google Scholar

  3. Pushpita Chatterjee
    View author publications

    You can also search for this author in PubMed Google Scholar

  4. Nadine Shillingford
    View author publications

    You can also search for this author in PubMed Google Scholar

Corresponding author

Correspondence to Debashis Das .

Editor information

Editors and Affiliations

  1. Khalifa University, Abu Dhabi, United Arab Emirates

    Deepak Puthal

  2. University of North Texas, Denton, TX, USA

    Saraju Mohanty

  3. University of Missouri at Kansas City, Kansas City, MO, USA

    Baek-Young Choi

Rights and permissions

Reprints and permissions

Copyright information

© 2024 IFIP International Federation for Information Processing

About this paper

Check for updates. Verify currency and authenticity via CrossMark

Cite this paper

Ghosh, U., Das, D., Chatterjee, P., Shillingford, N. (2024). Federated Edge-Cloud Framework for Heart Disease Risk Prediction Using Blockchain. In: Puthal, D., Mohanty, S., Choi, BY. (eds) Internet of Things. Advances in Information and Communication Technology. IFIPIoT 2023. IFIP Advances in Information and Communication Technology, vol 684. Springer, Cham. https://doi.org/10.1007/978-3-031-45882-8_21

Download citation

  • DOI: https://doi.org/10.1007/978-3-031-45882-8_21

  • Published:

  • Publisher Name: Springer, Cham

  • Print ISBN: 978-3-031-45881-1

  • Online ISBN: 978-3-031-45882-8

  • eBook Packages: Computer ScienceComputer Science (R0)

Publish with us

Policies and ethics

Societies and partnerships

Search

Navigation