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A Systematic Approach of Classification Model Based Prediction of Metabolic Disease Using Optical Coherence Tomography Images

  • M. VidhyasreeEmail author
  • R. Parameswari
Conference paper
Part of the Lecture Notes on Data Engineering and Communications Technologies book series (LNDECT, volume 35)

Abstract

Data mining is defined as the upcoming field that consists of certain tools and techniques to be implemented with certain data sets taken from the different sources to foresee the hidden information. The data mining is the huge upcoming field has attracted many fields under its influence. In the applications of data mining, health care is a very important application to be taken account. Healthcare is defined as the service provides the health maintenance and earlier disease prediction and also provides high quality treatments to prevent disease. Human body consists of a number of cells constituted to form organs and the organs connected to form the organ system. This system should be interconnected to work properly. The human body should be nourished properly by balanced diet and the healthy lifestyle. The function of the human body is disturbed by some external factors called disease. The metabolic disease is the collection of five different disorders such as high blood pressure, heart problems, obesity and insulin resistance. The Optical Coherence Tomography images of eyes are considered to predict the chronic conditions of the body accurately in the eyes. The main focus of this work is to detect diabetes through the retina images. This paper mainly reflects detection of diabetes using retina images. In this paper the classification techniques are analyzed using orange data mining tool to find the best classification technique based on the individual technique’s prediction accuracy.

Keywords

Data mining Classification techniques Prediction accuracy Image features 

References

  1. 1.
    Samant, P., Agarwal, R.: Machine learning techniques of medical diagonisis of diabetes using iris images. Comput. Methods Programs Programs Biomed. 1(4), 1–27 (2018)Google Scholar
  2. 2.
    Samant, P., Agarwal, R.: Diagonisis of diabetes using computer methods: soft computing methods for diabetes detection using iris. World Acad. Sci. Eng. Technol.: Int. J. Med. Health Biomed. Bioeng. Pharmacheutical Eng. 11(2), 57–62 (2017)Google Scholar
  3. 3.
    Somasundaram, S.K.: A machine learning ensemble classifier for early prediction of diabetic retinopathy. Int. J. Med. Sci. 41(201), 1–12 (2017)Google Scholar
  4. 4.
    Cho, Y., Lee, S., Woo, S.: The Krisch Lablician edge detection for predicting iris based disease. In: Proceedings of 2017 IEEE International Conference of Computer Suported Cooperative Design Work (2017)Google Scholar
  5. 5.
    Admin, J.: A method for detection and classification of diabetic retinopathy using structural predictors of bright lesions. J. Med. Sci. 19(6), 555–560 (2017)Google Scholar
  6. 6.
    Mozam, F.: Multiscale segmentation of excutation retinal images using contextual cues and ensemble classification. Biomed. Signal Process. 2(35), 52–60 (2017)Google Scholar
  7. 7.
    Perta, H.: Convolution neural network for diabetic retinopathy. Procedia Comput. Sci. 90, 200–205 (2016)CrossRefGoogle Scholar
  8. 8.
    Samnt, P., Agarwal, R.: Comparative analysis of classification based algorithms diabetes diagonosis using iris images. J. Med. Eng. Technol. 42, 1–9 (2018)CrossRefGoogle Scholar
  9. 9.
    Kaur, J., Sinha, H.P.: Automatic detection of diabetic retinopathy using fundus image analysis. Int. J. Comput. Sci. Technol. 3(4), 4794–4799 (2012)Google Scholar
  10. 10.
    Faust, O.: Algorithm for automted detection of diabetic retinopathy using digital fundus images. Int. J. Med. Sci. 36, 1–13 (2010)Google Scholar
  11. 11.
    Mangrulkar, R.S.: Renel image classification technique for debiets identification. In: IEEE International Conference on C Intelligent Computing and Control, pp. 190–194 (2017)Google Scholar
  12. 12.
    Suo, Q.: Personalized disease prediction using a CNN based similarity learning method. In: IEEE International Conference Bioinformatics and Biomedicine, pp. 811–817 (2017)Google Scholar
  13. 13.
    Dangare, C.S.: Improved study of heart disease prediction system using data mining classification techniques. Int. J. Comput. Appl. 41(10), 44–49 (2012)Google Scholar
  14. 14.
    Saranya, M.S.: Intelligent data storage system using machine learning approach. In: IEEE International Conference on Advanced Computing, pp. 191–195 (2016)Google Scholar
  15. 15.
    Rajliwall, N.S., et al.: Chronic disease risk monitoring based on an innovative predictive modelling framework. In: IEEE Symposium Series on Computational Intelligence, pp. 1–8 (2017)Google Scholar
  16. 16.
  17. 17.
  18. 18.
  19. 19.
  20. 20.
  21. 21.
  22. 22.
  23. 23.

Copyright information

© Springer Nature Switzerland AG 2020

Authors and Affiliations

  1. 1.Technology Advanced StudiesVels Institute of ScienceTamilnaduIndia

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