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)


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.


Machine learning Metrics Accuracy ANN PSO SVM RVM 


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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

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