Partition and Hierarchical Based Clustering Techniques for Analysis of Neonatal Data

  • Nikhit Mago
  • Rudresh D. Shirwaikar
  • U. Dinesh Acharya
  • K. Govardhan Hegde
  • Leslie Edward S. Lewis
  • M. Shivakumar
Conference paper
Part of the Lecture Notes in Networks and Systems book series (LNNS, volume 14)

Abstract

With the increase of data in the medical domain over the years, it is extremely crucial that we analyze useful information and recognize patterns that can be used by the clinicians for better diagnosis of diseases. Clustering is a Machine Learning technique that can be used to categorize data into compact and dissimilar clusters to gain some meaningful insight. This paper uses partition and hierarchical based clustering techniques to cluster neonatal data into different clusters and identify the role of each cluster. Clustering discovers hidden knowledge which helps neonatologists in identifying neonates who are at risk and also helps in neonatal diagnosis. In addition, this paper also evaluates the number of clusters to be formed for the techniques using Silhouette Coefficient.

Keywords

Partition based clustering Hierarchical based clustering Silhouette coefficient Neonate K-means K-medoids 

Notes

Acknowledgements

Authors are deeply indebted to Manipal Institute of Technology and Manipal University for providing an opportunity to demonstrate the research work.

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Copyright information

© Springer Nature Singapore Pte Ltd. 2018

Authors and Affiliations

  • Nikhit Mago
    • 1
    • 2
  • Rudresh D. Shirwaikar
    • 1
    • 2
  • U. Dinesh Acharya
    • 1
    • 2
  • K. Govardhan Hegde
    • 1
    • 2
  • Leslie Edward S. Lewis
    • 1
    • 2
  • M. Shivakumar
    • 1
    • 2
  1. 1.Department of Computer Science and EngineeringMIT, Manipal UniversityManipalIndia
  2. 2.Department of PediatricKMCManipalIndia

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