Advertisement

Data Processing and Analytics in FC for Healthcare 4.0

  • Khushi Shah
  • Preet Modi
  • Jitendra Bhatia
Chapter
  • 19 Downloads
Part of the Signals and Communication Technology book series (SCT)

Abstract

The integration of IoT and ML led to the development of 4.0 technology. With the help of 4.0 technology, we can deliver the benefits of IoT and ML to Healthcare industry. Healthcare 4.0 focuses on precise, timely, and effective medicine using IoT and ML. Data analysis and data processing play a crucial role in managing and exertion of this unstructured data being generated by device interactions. The two primary challenges with performing data analysis on cloud are burdensome load on cloud and the slow response time. To overcome these issues, the fog or edge computing (FC) is introduced. FC uses edge devices to perform considerable amount of computation, storage, and communication for data that needs to be immediately processed. It overcomes issues of costly bandwidths by offloading network traffic from the main channel, overcomes limitations of computing power, and also protects the sensitive IoT data.

References

  1. 1.
    Menon, N. R., & Patil, A. P. (2016). Health care of senior citizens in Indian scenario: A technological perspective. In 2016 International Conference on ICT in Business Industry & Government (ICTBIG) (pp. 1–3). Piscataway, NJ: IEEE.Google Scholar
  2. 2.
    Products-data brief. Retrieved February 15, 2020, from https://www.cdc.gov/nchs/products/databriefs/db234.htm
  3. 3.
    Chanchaichujit, J., Tan, A., Meng, F., & Eaimkhong, S. (2019). An introduction to healthcare 4.0. In Healthcare 4.0 (pp. 1–15). Berlin: Springer.Google Scholar
  4. 4.
    IoT device installations trend. Retrieved February 15, 2020, from https://www.researchgate.net/figure/oT-device-installations-trend_fig1_322138675
  5. 5.
    The AI/ML use cases investors are betting on in healthcare. Retrieved February 15, 2020, from https://rockhealth.com/reports/the-ai-ml-use-cases-investors-are-betting-on-in-healthcare/
  6. 6.
    Zhou, F., Duh, H. B.-L., & Billinghurst, M. (2008). Trends in augmented reality tracking, interaction and display: A review of ten years of ISMAR. In Proceedings of the 7th IEEE/ACM International Symposium on Mixed and Augmented Reality (pp. 193–202). Washington, DC: IEEE Computer Society.Google Scholar
  7. 7.
    Hsieh, M. C., & Lee, J. J. (2018). Preliminary study of VR and AR applications in medical and healthcare education. Journal of Nursing and Health Studies, 3(1), 1.Google Scholar
  8. 8.
    Alloghani, M., Al-Jumeily, D., Hussain, A., Aljaaf, A. J., Mustafina, J., & Petrov, E. (2018). Healthcare services innovations based on the state of the art technology trend industry 4.0. In 2018 11th International Conference on Developments in eSystems Engineering (DeSE) (pp. 64–70). Piscataway, NJ: IEEE.Google Scholar
  9. 9.
    Tanwar, S., Tyagi, S., & Kumar, N. (2019). Multimedia Big Data computing for IoT applications: Concepts, paradigms and solutions (Vol. 163). Berlin: Springer.Google Scholar
  10. 10.
    Raghupathi, W., & Raghupathi, V. (2014). Big data analytics in healthcare: Promise and potential. Health Information Science and Systems, 2(1), 3.Google Scholar
  11. 11.
    Dash, S., Shakyawar, S. K., Sharma, M., & Kaushik, S. (2019). Big data in healthcare: Management, analysis and future prospects. Journal of Big Data, 6(1), 54.Google Scholar
  12. 12.
    Wang, Y., & Hajli, N. (2017). Exploring the path to big data analytics success in healthcare. Journal of Business Research, 70, 287–299.Google Scholar
  13. 13.
    Lakshmanachari, S., Srihari, C., Sudhakar, A., & Nalajala, P. (2017). Design and implementation of cloud based patient health care monitoring systems using IoT. In 2017 International Conference on Energy, Communication, Data Analytics and Soft Computing (ICECDS) (pp. 3713–3717). Piscataway, NJ: IEEE.Google Scholar
  14. 14.
    Bhatia, J., & Kumhar, M. (2015). Perspective study on load balancing paradigms in cloud computing. IJCSC, 6(1), 112–120.Google Scholar
  15. 15.
    Bhatia, J., Mehta, R., & Bhavsar, M. (2017). Variants of software defined network (SDN) based load balancing in cloud computing: A quick review. In Future internet technologies and trends (pp. 164–173) Cham: Springer.Google Scholar
  16. 16.
    Kumari, A., Tanwar, S., Tyagi, S., & Kumar, N. (2018). Fog computing for healthcare 4.0 environment: Opportunities and challenges. Computers & Electrical Engineering, 72, 1–13.Google Scholar
  17. 17.
    Sughasiny, M., & Rajeshwari, J. (2018). Application of machine learning techniques, big data analytics in health care sector—a literature survey. In 2018 2nd International Conference on I-SMAC (IoT in Social, Mobile, Analytics and Cloud), 2018 2nd International Conference on (I-SMAC) I-SMAC (IoT in Social, Mobile, Analytics and Cloud) (I-SMAC) (pp. 741–749). Piscataway, NJ: IEEE.Google Scholar
  18. 18.
    Verma, P., & Sood, S. K. (2018). Fog assisted-IoT enabled patient health monitoring in smart homes. IEEE Internet of Things Journal, 5(3), 1789–1796.Google Scholar
  19. 19.
    Kreutz, D., Ramos, F., Verissimo, P., Rothenberg, C. E., Azodolmolky, S., & Uhlig, S. (2014). Software-defined networking: A comprehensive survey. Proceedings of the IEEE 103(1), 14–76.Google Scholar
  20. 20.
    Bhatia, J., Govani, R., & Bhavsar, M. (2018). Software defined networking: From theory to practice. In 2018 Fifth International Conference on Parallel, Distributed and Grid Computing (PDGC) (pp. 789–794).Google Scholar
  21. 21.
    Srilakshmi, A., Mohanapriya, P., Harini, D., & Geetha, K. (2019). IoT based smart health care system to prevent security attacks in SDN. In 2019 Fifth International Conference on Electrical Energy Systems (ICEES) (pp. 1–7). IEEE.Google Scholar
  22. 22.
    Bhatia, J., Dave, R., Bhayani, H., Tanwar, S., & Nayyar, A. (2020). SDN-based real-time urban traffic analysis in VANET environment. Computer Communications, 149, 162–175.Google Scholar
  23. 23.
    Bhatia, J., Modi, Y., Tanwar, S., & Bhavsar, M. (2019). Software defined vehicular networks: A comprehensive review. International Journal of Communication Systems, 32(12), e4005.Google Scholar
  24. 24.
    Alamri, A. (2019). Big data with integrated cloud computing for prediction of health conditions. In 2019 International Conference on Platform Technology and Service (PlatCon) (pp. 1–6). Piscataway, NJ: IEEE.Google Scholar
  25. 25.
    Krishnamachari, L., Estrin, D., & Wicker, S. (2002). The impact of data aggregation in wireless sensor networks. In Proceedings of the 22nd International Conference on Distributed Computing Systems Workshops (Vol. 578). Piscataway, NJ: IEEE.Google Scholar
  26. 26.
    Bhatia, J., Kakadia, P., Bhavsar, M., & Tanwar, S. (2019). SDN-enabled network coding based secure data dissemination in VANET environment. IEEE Internet of Things Journal, 1–1.Google Scholar
  27. 27.
    Vora, J., Nayyar, A., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., & Rodrigues, J. J. (2018). BHEEM: A blockchain-based framework for securing electronic health records. In 2018 IEEE GLOBECOM Workshops (GC Wkshps) (pp. 1–6).Google Scholar
  28. 28.
    Ekblaw, A., Azaria, A., Halamka, J. D., & Lippman, A. (2016). A case study for blockchain in healthcare: “MedRec?” prototype for electronic health records and medical research data. In Proceedings of IEEE open & Big Data Conference (Vol. 13, p. 13).Google Scholar
  29. 29.
    Famili, A., Shen, W. M., Weber, R., & Simoudis, E. (1997). Data preprocessing and intelligent data analysis. Intelligent Data Analysis, 1(1), 3–23.Google Scholar
  30. 30.
    Mehta, N., & Pandit, A. (2018). Concurrence of big data analytics and healthcare: A systematic review. International Journal of Medical Informatics, 114, 57–65.Google Scholar
  31. 31.
    Bhatia, J. B. (2015). A dynamic model for load balancing in cloud infrastructure. Nirma University Journal of Engineering and Technology, 4(1), 15.Google Scholar
  32. 32.
    MS Windows NT kernel description. Retrieved January 16, 2020, from https://en.wikipedia.org/wiki/Machine_learning
  33. 33.
    Miotto, R., Wang, F., Wang, S., Jiang, X., & Dudley, J. T. (2018). Deep learning for healthcare: Review, opportunities and challenges. Briefings in Bioinformatics, 19(6), 1236–1246.Google Scholar
  34. 34.
    Wiens, J., & Shenoy, E. S. (2018). Machine learning for healthcare: On the verge of a major shift in healthcare epidemiology. Clinical Infectious Diseases, 66(1), 149–153.Google Scholar
  35. 35.
    Gupta, D., Khare, S., & Aggarwal, A. (2016). A method to predict diagnostic codes for chronic diseases using machine learning techniques. In 2016 International Conference on Computing, Communication and Automation (ICCCA) (pp. 281–287). Piscataway, NJ: IEEE.Google Scholar
  36. 36.
    Araújo, F. H., Santana, A. M., & Neto, P. D. A. S. (2016). Using machine learning to support healthcare professionals in making preauthorisation decisions. International Journal of Medical Informatics, 94, 1–7.Google Scholar
  37. 37.
    Chen, M., Li, W., Hao, Y., Qian, Y., & Humar, I. (2018). Edge cognitive computing based smart healthcare system. Future Generation Computer Systems, 86, 403–411.Google Scholar
  38. 38.
    Dolatabadi, A. D., Khadem, S. E. Z., & Asl, B. M. (2017). Automated diagnosis of coronary artery disease (CAD) patients using optimized SVM. Computer Methods and Programs in Biomedicine, 138, 117–126.Google Scholar
  39. 39.
    Varatharajan, R., Manogaran, G., & Priyan, M. K. (2018). A big data classification approach using LDA with an enhanced SVM method for ECG signals in cloud computing. Multimedia Tools and Applications, 77(8), 10195–10215.Google Scholar
  40. 40.
    Zhong, H., & Xiao, J. (2017). Enhancing health risk prediction with deep learning on big data and revised fusion node paradigm. Scientific Programming, 2017, 1901876.Google Scholar
  41. 41.
    Maini, E., Venkateswarlu, B., & Gupta, A. (2018). Applying machine learning algorithms to develop a universal cardiovascular disease prediction system. In International Conference on Intelligent Data Communication Technologies and Internet of Things (pp. 627–632). Berlin: Springer.Google Scholar
  42. 42.
    Abinash, M. J., & Vasudevan, V. (2018). A study on wrapper-based feature selection algorithm for leukemia dataset. In Intelligent Engineering Informatics (pp. 311–321). Berlin: Springer.Google Scholar
  43. 43.
    Kannan, R., & Vasanthi, V. (2019). Machine learning algorithms with ROC curve for predicting and diagnosing the heart disease. In Soft Computing and Medical Bioinformatics (pp. 63–72). Berlin: Springer.Google Scholar
  44. 44.
    Nijeweme-d’Hollosy, W. O., van Velsen, L., Poel, M., Groothuis-Oudshoorn, C. G. M., Soer, R., & Hermens, H. (2018). Evaluation of three machine learning models for self-referral decision support on low back pain in primary care. International Journal of Medical Informatics, 110, 31–41.Google Scholar
  45. 45.
    Ndaba, M., Pillay, A. W., & Ezugwu, A. E. (2018). An improved generalized regression neural network for type II diabetes classification. In International Conference on Computational Science and Its Applications (pp. 659–671). Berlin: Springer.Google Scholar
  46. 46.
    Nair, L. R., Shetty, S. D., & Shetty, S. D. (2018). Applying spark based machine learning model on streaming big data for health status prediction. Computers & Electrical Engineering, 65, 393–399.Google Scholar
  47. 47.
    Kim, J. S., Merrill, R. K., Arvind, V., Kaji, D., Pasik, S. D., Nwachukwu, C. C., et al. (2018). Examining the ability of artificial neural networks machine learning models to accurately predict complications following posterior lumbar spine fusion. Spine, 43(12), 853.Google Scholar
  48. 48.
    Singh, K., & Malhotra J. (2019). IoT and cloud computing based automatic epileptic seizure detection using HOS features based random forest classification. Journal of Ambient Intelligence and Humanized Computing, 1–16.Google Scholar
  49. 49.
    Yahyaoui, A., Rasheed, J., Jamil, A., & Yesiltepe, M. (2019). A decision support system for diabetes prediction using machine learning and deep learning techniques. In 2019 1st International Informatics and Software Engineering Conference (UBMYK) (pp. 1–4). Piscataway, NJ: IEEE.Google Scholar
  50. 50.
    Jadhav, S., Kasar, R., Lade, N., Patil, M., & Kolte, S. (2019). Disease prediction by machine learning from healthcare communities.Google Scholar
  51. 51.
    Kraemer, F. A., Braten, A. E., Tamkittikhun, N., & Palma, D. (2017). Fog computing in healthcare—a review and discussion. IEEE Access, 5, 9206–9222Google Scholar
  52. 52.
    Vora, J., Italiya, P., Tanwar, S., Tyagi, S., Kumar, N., Obaidat, M. S., et al. (2018). Ensuring privacy and security in e-health records. In 2018 International Conference on Computer, Information and Telecommunication Systems (CITS) (pp. 1–5). Piscataway, NJ: IEEE.Google Scholar
  53. 53.
    Stojmenovic, I., & Wen, S. (2014). The fog computing paradigm: Scenarios and security issues. In 2014 Federated Conference on Computer Science and Information Systems (pp. 1–8). Piscataway, NJ: IEEE.Google Scholar
  54. 54.
    Balfanz, D., Smetters, D. Stewart, P., & Wong, H. (2002). Talking to strangers: Authentication in ad-hoc wireless networks. In Symposium on Network and Distributed Systems Security (NDSS’02). San Diego, CA.Google Scholar
  55. 55.
    Yi, S., Qin, Z., & Li, Q. (2015). Security and privacy issues of fog computing: A survey. In International conference on wireless algorithms, systems, and applications (pp. 685–695). Berlin: Springer.Google Scholar
  56. 56.
    Pace, P., Aloi, G., Gravina, R., Caliciuri, G., Fortino, G., & Liotta, A. (2018). An edge-based architecture to support efficient applications for healthcare industry 4.0. IEEE Transactions on Industrial Informatics, 15(1), 481–489.Google Scholar

Copyright information

© Springer Nature Switzerland AG 2021

Authors and Affiliations

  • Khushi Shah
    • 1
  • Preet Modi
    • 1
  • Jitendra Bhatia
    • 1
  1. 1.Vishwakarma Government Engineering CollegeAhmedabadIndia

Personalised recommendations