A Review of Semantic Annotation Models for Analysis of Healthcare Data Based on Data Mining Techniques

  • M. Manonmani
  • Sarojini BalakrishnanEmail author
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1054)


The evolution of medial data has made interaction and communication between devices very important and need of the hour to address the problems and requirements of the people in the medical domain. It seems to be an important task to overcome the compatibility issues between the communicating devices in the healthcare sector and provide meaningful solutions to the users of medical data. In the present era, interconnection of various sensors and devices in the medical domain is possible to a great extent with the advent of Internet. In the field of medicine, the major problems, viz. interoperability of heterogeneous medical devices, security of the patient information, and personalized visualization of the processed data seems to be very vital. This paper presents an analysis of the different data mining techniques applied in healthcare domain and semantic annotation of healthcare data for overcoming the issue of interoperability. The paper also discusses about the proposed research work which includes creating a semantic model for handling heterogeneous healthcare data and the application of feature selection algorithms and classification algorithms for medical diagnosis.


Medical domain Internet Interoperability Sensors Semantic model Data mining techniques 


  1. 1.
    Ringsqunadi, M., et al.: Semantic-guided feature selection for industrial automation systems. In: International Semantic Web Conference, Springer, LNCS 9367, pp. 225–240 (2015)Google Scholar
  2. 2.
    Mahdavinejad, M.S., Rezvan, M., et al.: Machine learning for internet of things data analysis: a survey. Digit. Commun. Netw. 4, 161–175 (2018)CrossRefGoogle Scholar
  3. 3.
    Jabbar, S., Ullah, F., Khalid, S., Khan, M., Han, K.: Semantic interoperability in heterogeneous IoT infrastructure for healthcare. Wireless Communications and Mobile Computing, 10 pages (2017)Google Scholar
  4. 4.
    Joshi, M., et al.: An application of IoT on Hungarian database using Data mining Techniques: a collaborative approach. In: 2017 3rd International Conference on Advances in Computing, Communication & Automation (ICACCA), pp. 1–6. IEEE (2017)Google Scholar
  5. 5.
    Gia, T.N., et al.: Fog computing in healthcare internet of things: a case study on ECG feature extraction. In: 2015 IEEE International Conference on Computer and Information Technology; Ubiquitous Computing and Communications; Dependable, Autonomic and Secure Computing; Pervasive Intelligence and Computing, pp. 356–363 (2015)Google Scholar
  6. 6.
    Ma, Y., et al.: Big health application system based on health internet of things and big data. IEEE Access 5, 7885–7897 (2017)CrossRefGoogle Scholar
  7. 7.
    Chui, K.T., et al.: Disease diagnosis in smart healthcare: innovation, technologies and applications. Sustainability 9, 2–23 (2017)CrossRefGoogle Scholar
  8. 8.
    Antunes, M., Gomes, D., Aguiar, R.: Towards IoT data classification through semantic features. Future Gener. Comput. Syst. 20 pages (2017)Google Scholar
  9. 9.
    Sharma, D., et al.: Evaluation of stemming and stop word techniques on text classification problem. Int. J. Sci. Res. Comput. Sci. Eng. 3(2), 1–4 (2015)Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.Department of Computer ScienceAvinashilingam Institute for Home Science and Higher Education for WomenCoimbatoreIndia

Personalised recommendations