Study of Classification Techniques on Medical Datasets

  • Girish Kumar SinghEmail author
  • Rahul K. Jain
  • Prabhati Dubey
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 810)


Medical science is using digital equipment and generates and gathers large volume of data. These medical datasets are analyzed to get useful information which helps in making decision about diagnosis and treatment. Data mining techniques solve the problem of knowledge extraction from databases from different sources. Several data mining methodologies like Classification, Clustering are used to analyze the data. Classification is a technique used in prediction and to classify the unknown data to a class. This paper presents a study of application of classification algorithms on different kinds of medical datasets.


Classification Medical dataset k-neighbor Neural network SVM 


  1. 1.
    Vanaja, S., Rameshkumar, K.: Performance analysis of classification algorithms on medical diagnoses-a survey. J. Comput. Sci. 30–52 (2014)CrossRefGoogle Scholar
  2. 2.
    Quinlan, J.R.: Induction of Decision trees, pp. 81–106. Kluwer Academic Publishers (1986)Google Scholar
  3. 3.
    Kotsiantis, S., Kanellopoulos, D.: Association rules mining. A recent overview. Int. Trans. Comput. Sci. Eng. 71–82 (2006)Google Scholar
  4. 4.
    Boser, B.E., Guyon, I.M., Vapnik, V.N.: A training algorithm for optimal margin classifiers. In: Proceedings of the 5th Annual ACM Workshop on Computational Learning Theory, New York, USA, pp. 144–152 (1992)Google Scholar
  5. 5.
    Eiben, A.E., et al.: Genetic algorithms with multi-parents recombination. In: The Third Conference on Parallel Problem Solving from Nature, pp. 78–87 (1994)CrossRefGoogle Scholar
  6. 6.
    Hopfield, J.J.: Artificial neural networks. IEEE Circuit Device Mag. 3–10 (1988)CrossRefGoogle Scholar
  7. 7.
    Pawlak, Z.: Rough set. Int. J. Comput. Inf. Sci. 341–356 (1982)Google Scholar
  8. 8.
    Zadeh, L.A.: Fuzzy Sets, pp. 338–353. Elsevier (1965)Google Scholar
  9. 9.
    Altman, N.M.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 175–185 (1992)MathSciNetGoogle Scholar
  10. 10.
    De Mántaras, R.L.: A distance-based attribute selection measure for decision tree induction, pp. 81–92. Kluwer Academic Publishers-Plenum Publishers (1991)Google Scholar
  11. 11.
    Prasad, N.: Gain ratio as attribute selection measure in elegant decision tree to predict precipitation. In: Modelling and Simulation (EUROSIM), pp. 141–150 (2008)Google Scholar
  12. 12.
    Zhang, S., et al.: A strategy for attributes selection in cost-sensitive decision trees induction. In: Computer and Information Technology Workshops, pp. 8–13 (2008)Google Scholar
  13. 13.
    Su, J., Zhang, H.: A fast decision tree learning algorithm. In: Proceedings of AAAI’06 Proceedings of the 21st National Conference on Artificial Intelligence, vol. 1, pp. 500–505 (2006)Google Scholar
  14. 14.
    Ismanto, H., Wardoyo, R.: Analysis of C4.5 and K-nearest neighbor (KNN) method on algorithm of clustering for deciding mainstay area. IOSR J. Comput. Eng. 86–92 (2016)Google Scholar
  15. 15.
    Orponene, P.: Computational complexity of networks: a survey. Nordic J. Comput. 94–110 (1994)Google Scholar
  16. 16.
    UCI Machine Learning Repository.

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • Girish Kumar Singh
    • 1
    Email author
  • Rahul K. Jain
    • 2
  • Prabhati Dubey
    • 1
  1. 1.Department of Computer Science and ApplicationsDr. Harisingh Gour UniversitySagarIndia
  2. 2.B.T.I.R.T. College, RGPV UniversityBhopalIndia

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