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

Research output: Chapter in Book/Report/Conference proceedingChapter

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.

Original languageEnglish
Title of host publicationLecture Notes in Networks and Systems
PublisherSpringer Paris
Pages345-355
Number of pages11
DOIs
Publication statusPublished - 01-01-2018

Publication series

NameLecture Notes in Networks and Systems
Volume14
ISSN (Print)2367-3370
ISSN (Electronic)2367-3389

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All Science Journal Classification (ASJC) codes

  • Computer Networks and Communications
  • Signal Processing
  • Control and Systems Engineering

Cite this

Mago, N., Shirwaikar, R. D., Dinesh Acharya, U., Govardhan Hegde, K., Lewis, L. E. S., & Shivakumar, M. (2018). Partition and hierarchical based clustering techniques for analysis of neonatal data. In Lecture Notes in Networks and Systems (pp. 345-355). (Lecture Notes in Networks and Systems; Vol. 14). Springer Paris. https://doi.org/10.1007/978-981-10-5146-3_32