Abstract
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.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Vanaja, S., Rameshkumar, K.: Performance analysis of classification algorithms on medical diagnoses-a survey. J. Comput. Sci. 30–52 (2014)
Quinlan, J.R.: Induction of Decision trees, pp. 81–106. Kluwer Academic Publishers (1986)
Kotsiantis, S., Kanellopoulos, D.: Association rules mining. A recent overview. Int. Trans. Comput. Sci. Eng. 71–82 (2006)
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)
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)
Hopfield, J.J.: Artificial neural networks. IEEE Circuit Device Mag. 3–10 (1988)
Pawlak, Z.: Rough set. Int. J. Comput. Inf. Sci. 341–356 (1982)
Zadeh, L.A.: Fuzzy Sets, pp. 338–353. Elsevier (1965)
Altman, N.M.: An introduction to kernel and nearest-neighbor nonparametric regression. Am. Stat. 175–185 (1992)
De Mántaras, R.L.: A distance-based attribute selection measure for decision tree induction, pp. 81–92. Kluwer Academic Publishers-Plenum Publishers (1991)
Prasad, N.: Gain ratio as attribute selection measure in elegant decision tree to predict precipitation. In: Modelling and Simulation (EUROSIM), pp. 141–150 (2008)
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)
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)
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)
Orponene, P.: Computational complexity of networks: a survey. Nordic J. Comput. 94–110 (1994)
UCI Machine Learning Repository. http://archive.ics.uci.edu/ml/datasets.html
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2019 Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Singh, G.K., Jain, R.K., Dubey, P. (2019). Study of Classification Techniques on Medical Datasets. In: Iyer, B., Nalbalwar, S., Pathak, N. (eds) Computing, Communication and Signal Processing . Advances in Intelligent Systems and Computing, vol 810. Springer, Singapore. https://doi.org/10.1007/978-981-13-1513-8_57
Download citation
DOI: https://doi.org/10.1007/978-981-13-1513-8_57
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-13-1512-1
Online ISBN: 978-981-13-1513-8
eBook Packages: EngineeringEngineering (R0)