A new healthcare diagnosis system using an IoT-based fuzzy classifier with FPGA

  • Sambit SatpathyEmail author
  • Prakash Mohan
  • Sanchali Das
  • Swapan Debbarma


In this study, an Internet of Things (IoT)-based analysis system is proposed, which can be used to design a consumer electronic device. This IoT-based analysis system will alert the user when any of the parameters related to his/her health is above or below the normal range. The data collected using this system are uploaded to the cloud using a mobile application and then transferred to the field-programmable gate array (FPGA) analysis system. The raw data are computed and processed by the FPGA system, and pathological conditions are displayed on the patient’s wearable IoT device. The novelty of this work lies in the development of a fuzzy classifier, which indicates the pathological conditions of diseases with a higher accuracy. The FPGA implementation of the fuzzy classifier results in a lower execution time in comparison with that of previous models, such as K-nearest neighbour, decision tree, support vector machine, and naive Bayes.


IoT Healthcare system Disease prediction classification FPGA 



  1. 1.
    Dumka A, Sah A (2019) Smart ambulance system using concept of big data and internet of things. In: Healthcare data analytics and management. Academic Press, New York, pp 155–176CrossRefGoogle Scholar
  2. 2.
    Vijayakumar V, Malathi D, Subramaniyaswamy V, Saravanan P, Logesh R (2019) Fog computing-based intelligent healthcare system for the detection and prevention of mosquito-borne diseases. Comput Hum Behav 100:275–285CrossRefGoogle Scholar
  3. 3.
    Lee B, Lee JH (2017) Blockchain-based secure firmware update for embedded devices in an internet of things environment. J Supercomput 73(3):1152–1167CrossRefGoogle Scholar
  4. 4.
    Gope P, Hwang T (2015) A realistic lightweight authentication protocol preserving strong anonymity for securing RFID system. Comput Secur 55:271–280CrossRefGoogle Scholar
  5. 5.
    Gope P, Hwang T et al (2015) Untraceable sensor movement in distributed IoT infrastructure. IEEE Sens J 15(9):5340–5348CrossRefGoogle Scholar
  6. 6.
    Hussain HM, Benkrid K, Seker H, Erdogan AT (2011) FPGA implementation of k-means algorithm for bioinformatics application: an accelerated approach to clustering microarray data. In: 2011 NASA/ESA Conference on Adaptive Hardware and Systems (AHS), pp 248–255Google Scholar
  7. 7.
    Jain VK, Kumar S (2018) Rough set based intelligent approach for identification of H1N1 suspect using social media. Kuwait J Sci 45(2):8–14Google Scholar
  8. 8.
    Kalid N, Zaidan AA, Zaidan BB, Salman OH, Hashim M, Muzammil H (2018) Based real time remote health monitoring systems: a review on patients prioritization and related “Big Data” using body sensors information and communication technology. J Med Syst 42(2):30CrossRefGoogle Scholar
  9. 9.
    Safkhani M, Bagheri N (2017) Passive secret disclosure attack on an ultralightweight authentication protocol for internet of things. J Supercomput 73(8):3579–3585CrossRefGoogle Scholar
  10. 10.
    Peng L, Peng M, Liao B, Huang G, Li W, Xie D (2018) The advances and challenges of deep learning application in biological big data processing. Curr Bioinform 13(4):352–359CrossRefGoogle Scholar
  11. 11.
    Rajkumar N, Palanichamy J (2015) Optimized construction of various classification models for the diagnosis of thyroid problems in human beings. Kuwait J Sci 42(2):189–205Google Scholar
  12. 12.
    Data base used for cardiovascular disease (2018).
  13. 13.
    Singh P, Singh S, Pandi-Jain GS (2018) effective heart disease prediction sys- tem using data mining techniques. Int J Nanomed 13:121CrossRefGoogle Scholar
  14. 14.
    Sultana M, Haider A, Uddin MS (2016) Analysis of data mining techniques for heart disease prediction. In: 2016 3rd International Conference on Electrical Engineering and Information Communication Technology (ICEE–ICT), pp 1–5Google Scholar
  15. 15.
    Sundaravadivel P, Kougianos E, Mohanty SP, Ganapathiraju MK (2018) Everything you wanted to know about smart health care: evaluating the different technologies and components of the internet of things for better health. IEEE Consum Electron Mag 7(1):18–28CrossRefGoogle Scholar
  16. 16.
    Thapliyal H, Nath RK, Mohanty SP (2018) Smart home environment for mild cognitive impairment population: solutions to improve care and quality of life. IEEE Consum Electron Mag 7(1):68–76CrossRefGoogle Scholar
  17. 17.
    Verma P, Sood SK (2018) Cloud-centric IoT based disease diagnosis health- care framework. J Parallel Distrib Comput 116:27–38CrossRefGoogle Scholar
  18. 18.
    Verma P, Sood SK, Kalra S (2018) Cloud-centric IoT based student health- care monitoring framework. J Ambient Intell Hum Comput 9(5):1293–1309CrossRefGoogle Scholar
  19. 19.
    Weinstein R (2005) RFID: a technical overview and its application to the enterprise. IT Prof 7(3):27–33CrossRefGoogle Scholar

Copyright information

© Springer Science+Business Media, LLC, part of Springer Nature 2019

Authors and Affiliations

  • Sambit Satpathy
    • 1
    Email author
  • Prakash Mohan
    • 2
  • Sanchali Das
    • 1
    • 2
  • Swapan Debbarma
    • 1
  1. 1.CSENIT AgartalaJirania, AgartalaIndia
  2. 2.Data Science and Analytics CenterKarpagam College of EngineeringCoimbatoreIndia

Personalised recommendations