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A Study of Cataract Patient Data Using C5.0

  • Mamta Santosh NairEmail author
  • Umesh Kumar Pandey
Conference paper
  • 33 Downloads
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 1077)

Abstract

Cataract is one of the common problems among the humans related to eye. Clouding of lens termed as cataract leads to blindness. Various causes of cataract are identified by the ophthalmologist. Data mining has become popular in the past few years because of information extracted from the dataset using algorithm and computational capability. In this paper, cataract patients’ historical data is used to build the predictive model. C5.0 algorithm is one of the decision tree algorithms used for predictive modeling. Present study uses C5.0 method to predict cataract status on various parameters. Data used in this research paper is the primary data collected from the Raigad Maharashtra, India.

Keywords

Classification C5.0 algorithm Cataract 

References

  1. 1.
    WHO homepage. https://www.who.int/blindness/causes/priority/en/index1.html. Last accessed 25 May 2019
  2. 2.
    Gajpal, A.L., Pandey, U.K.: Identifying dropout factor order using C5.0 decision tree. Int. J. Adv. Res. Sci. Eng. 6(4) (2017). ISSN(o) 2319–8354. ISSN(P) 2319-8346Google Scholar
  3. 3.
    Kesavaraj, G., Sukumaran, S.: A study on classification techniques in data mining. IEEE-31661, July 4–6 (2013)Google Scholar
  4. 4.
    Pandya, R., Pandya, J.: C5.0 algorithm to improved decision tree with feature selection and reduced error pruning. Int. J. Comput. Appl. 117(16), 18–21 (2015)Google Scholar
  5. 5.
    Sattler, K.U., Dunemann, S.O.: SQL database primitives for decision tree classifiers. CIKM ’01 atlanta, ACM, GA USA (2001)Google Scholar
  6. 6.
    Sharma, H., Kumar, S.: A survey on decision tree algorithms of classification in data mining. Int. J. Sci. Res. (IJSR) 5 (2016)Google Scholar
  7. 7.
    Anyanwu, M.N., Shiva, S.G.: Comparative analysis of serial decision tree classification algorithms. Int. J. Comput. Sci. Secur. (IJCSS) 3(3)Google Scholar
  8. 8.
    Kaur, S., Grewal, A.K.: A review paper on data mining classification techniques for detection of lung cancer. Int. Res. J. Eng. Technol. (IRJET) 3(11) (2016). e-ISSN: 2395-0056, p-ISSN: 2395-0072Google Scholar
  9. 9.
    Pang, S.-L., Gong, J.-Z.: C5.0 classification algorithm and application on individual credit evaluation of banks. Syst. Eng.-Theory Pract. 29(12), 94–104Google Scholar
  10. 10.
    Alaoui, S.S., Labsiv, Y., Aksasse, B.: Classification algorithms in data mining. Int. J. Tomogr. Simul. 31, 34–44 (2018)Google Scholar
  11. 11.
    Kumar, S.V.K., Kiruthika, P.: An overview of classification algorithm in data mining. Int. J. Adv. Res. Comput. Commun. Eng. 4(12) (2015). ISSN (Online) 2278–1021 ISSN (Print) 2319-5940Google Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2020

Authors and Affiliations

  1. 1.MATS School of Information TechnologyMATS UniversityRaipurIndia

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