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

doi:10.1007/978-981-10-5146-3_32

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 proceeding › Chapter

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 language	English
Title of host publication	Lecture Notes in Networks and Systems
Publisher	Springer Paris
Pages	345-355
Number of pages	11
DOIs	https://doi.org/10.1007/978-981-10-5146-3_32
Publication status	Published - 01-01-2018

Publication series

Name	Lecture Notes in Networks and Systems
Volume	14
ISSN (Print)	2367-3370
ISSN (Electronic)	2367-3389

All Science Journal Classification (ASJC) codes

Computer Networks and Communications
Signal Processing
Control and Systems Engineering

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10.1007/978-981-10-5146-3_32

Cite this

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AU - Dinesh Acharya, U.

AU - Govardhan Hegde, K.

AU - Lewis, Leslie Edward S.

AU - Shivakumar, M.

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M3 - Chapter

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Partition and hierarchical based clustering techniques for analysis of neonatal data

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