Advertisement

Machine Learning on Medical Dataset

  • M. P. GopinathEmail author
  • S. L. Aarthy
  • Aditya Manchanda
  • Rishadh
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 862)

Abstract

Machine learning for medical decision-making is helpful when it is possible to achieve two conditions: a well-defined way to reach the diagnosis and high prediction accuracy. With the introduction of sensors in every aspect of daily live, there is a huge amount of data compiled (the present study concentrates on data from health), such quantity without any processing/information extraction technique means nothing. However, not every processing makes it possible to obtain something helpful from the raw data. In the present, the document evaluated three methodologies about machine learning (ANN + PSO, SVM, and RVM) in order to make a valuation on the performance over a set of data, and after the tests, the RVM methodology behaves the best.

Keywords

Machine learning Metrics Accuracy ANN PSO SVM RVM 

References

  1. 1.
    R. Agarwal, V. Dhar, Big data, data science, and analytics: the opportunity and challenge for IS research, 443–448(2014)Google Scholar
  2. 2.
    A.C. Azubogu et al., Wireless sensor networks for long distance pipeline monitoring. World Acad. Sci. Eng. Technol. Int. J. Electr. Comput. Energ. Electron. Commun. Eng. 7(3), 285–289 (2013)Google Scholar
  3. 3.
    W.G. Baxt, Use of an artificial neural network for the diagnosis of myocardial infarction. Ann. Intern. Med. 115(11), 843–848 (1991)CrossRefGoogle Scholar
  4. 4.
    D.M. Dutton, G.V. Conroy, A review of machine learning. Knowl. Eng. Rev. 12(4), 341–367 (1997)CrossRefGoogle Scholar
  5. 5.
    J.D. Halamka, Early experiences with big data at an academic medical center. Health Aff. 33(7), 1132–1138 (2014)CrossRefGoogle Scholar
  6. 6.
    L. Han, S. Luo, J. Yu, L. Pan, S. Chen, Rule extraction from support vector machines using ensemble learning approach: an application for diagnosis of diabetes. IEEE J. Biomed. Health Inform. 19(2), 728–734 (2015)CrossRefGoogle Scholar
  7. 7.
    M.I. Jordan, T.M. Mitchell, Machine learning: trends, perspectives, and prospects. Science 349(6245), 255–260 (2015)MathSciNetCrossRefGoogle Scholar
  8. 8.
    S.B. Kotsiantis, I.D. Zaharakis, P.E. Pintelas, Machine learning: a review of classification and combining techniques. Artif. Intell. Rev. 26(3), 159–190 (2006)CrossRefGoogle Scholar
  9. 9.
    R.V. Kulkarni, G.K. Venayagamoorthy, Particle swarm optimization in wireless-sensor networks: a brief survey. IEEE Trans. Syst. Man Cybern. Part C (Appl. Rev.) 41(2), 262–267 (2011)CrossRefGoogle Scholar
  10. 10.
    V.A. Kumari, R. Chitra, Classification of diabetes disease using support vector machine. Int. J. Eng. Res. Appl. 3(2), 1797–1801 (2013)Google Scholar
  11. 11.
    N. Murata, S. Yoshizawa, S.I. Amari, Network information criterion-determining the number of hidden units for an artificial neural network model. IEEE Trans. Neural Netw. 5(6), 865–872 (1994)CrossRefGoogle Scholar
  12. 12.
    E. Naidus, L.A. Celi, Big data in healthcare: are we close to it? Rev. Bras. de Terapia Intensiva 28(1), 8–10 (2016)Google Scholar
  13. 13.
    P. Neirotti, A. De Marco, A.C. Cagliano, G. Mangano, F. Scorrano, Current trends in smart city initiatives: some stylised facts. Cities 38, 25–36 (2014)CrossRefGoogle Scholar
  14. 14.
    L.G. Nongxa, Mathematical and statistical foundations and challenges of (big) data sciences. S. Afr. J. Sci. 113(3–4), 1–4 (2017)Google Scholar
  15. 15.
    R. Poli, J. Kennedy, T. Blackwell, Particle swarm optimization. Swarm Intell. 1(1), 33–57 (2007)CrossRefGoogle Scholar
  16. 16.
    M.S. Uzer, N. Yilmaz, O. Inan, Feature selection method based on artificial bee colony algorithm and support vector machines for medical datasets classification. Sci. World J. (2013)Google Scholar
  17. 17.
    B. Xue, M. Zhang, W.N. Browne, Particle swarm optimization for feature selection in classification: a multi-objective approach. IEEE Trans. Cybern. 43(6), 1656–1671 (2013)CrossRefGoogle Scholar

Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  • M. P. Gopinath
    • 1
    Email author
  • S. L. Aarthy
    • 2
  • Aditya Manchanda
    • 1
  • Rishadh
    • 1
  1. 1.School of Computer Science and EngineeringVellore Institute of TechnologyVelloreIndia
  2. 2.School of Information TechnologyVellore Institute of TechnologyVelloreIndia

Personalised recommendations