Cluster analysis for european neonatal jaundice

Title

Cluster analysis for european neonatal jaundice

Creator

Banu P.K.N.; Own H.S.; Olariu T.; Olariu I.

Description

The objective of this paper is to propose and analyze clustering techniques for neonatal jaundice which will help in grouping the babies of similar symptoms. A variety of methods have been introduced in the literature for neonatal jaundice classification and feature selection. As far as we know, clustering techniques are not used for neonatal jaundice data set. This paper studies and proposes clustering techniques such as K-Means, Genetic K-Means and Bat K-Means for jaundice disease. To find the number of clusters elbow method is used. The clusters are validated using RMSE, SI and HI. The experimental results carried out in this paper shows bat k-means clustering performs better than K-means and genetic K-means. 2018, Springer International Publishing AG.

Source

Advances in Intelligent Systems and Computing, Vol-633, pp. 408-419.

Date

2018-01-01

Publisher

Springer Verlag

Subject

Bat K-means; Clusters; Genetic K-means; K-means; Neonatal jaundice

Coverage

Banu P.K.N., Department of Computer Science, Christ University, Bengaluru, India; Own H.S., National Research Institute of Astronomy and Geophysics, Helwan, Egypt; Olariu T., Vasile Goldis Western University of Arad, Arad, Romania; Olariu I., Vasile Goldis Western University of Arad, Arad, Romania

Rights

Restricted Access

Relation

ISSN: 21945357; ISBN: 978-331962520-1

Format

Online

Language

English

Type

Conference paper

Identifier

https://doi.org/10.1007/978-3-319-62521-8_35

https://www.scopus.com/inward/record.uri?eid=2-s2.0-85029498457&doi=10.1007%2f978-3-319-62521-8_35&partnerID=40&md5=dc8ad46050e812a361eb55dae6a2d229