Article: Chicken swarm optimisation based clustering of biomedical documents and health records to improve telemedicine applications Journal: International Journal of Enterprise Network Management (IJENM) 2019 Vol.10 No.3/4 pp.305 - 328 Abstract: The aim of this paper is to develop an efficient ontology enabled chicken swarm optimisation (CSO) based clustering algorithm with dynamic dimension reduction (DDR) to efficiently cluster biomedical documents and health records to facilitate telemedicine applications. A total of 350 documents and health records are collected from PubMed repository for telemedicine applications. First, the documents are pre-processed via semantic annotation and concept mapping while term frequency and inverse gravity moment (TF-IGM) factor is used to improve document representation and the modified n-gram resolves the substitution and deletion malpractices. DDR technique reduces feature space dimension and prunes non-useful text features to increase the clustering accuracy by tackling the high dimensionality problem. Finally, the clusters are formed by CSO clustering. Experimental simulations prove that the CSO-DDR clustering model is significantly efficient than the traditional algorithms and ensures reliable and adaptive telemedicine applications with better clustering of biomedical documents and health records. Inderscience Publishers - linking academia, business and industry through research

Title: Chicken swarm optimisation based clustering of biomedical documents and health records to improve telemedicine applications

Authors: R. Sandhiya; M. Sundarambal

Addresses: Department of Information Technology, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, 641014, India ' Department of Electrical and Electronics Engineering, Coimbatore Institute of Technology, Coimbatore, Tamilnadu, 641014, India

Abstract: The aim of this paper is to develop an efficient ontology enabled chicken swarm optimisation (CSO) based clustering algorithm with dynamic dimension reduction (DDR) to efficiently cluster biomedical documents and health records to facilitate telemedicine applications. A total of 350 documents and health records are collected from PubMed repository for telemedicine applications. First, the documents are pre-processed via semantic annotation and concept mapping while term frequency and inverse gravity moment (TF-IGM) factor is used to improve document representation and the modified n-gram resolves the substitution and deletion malpractices. DDR technique reduces feature space dimension and prunes non-useful text features to increase the clustering accuracy by tackling the high dimensionality problem. Finally, the clusters are formed by CSO clustering. Experimental simulations prove that the CSO-DDR clustering model is significantly efficient than the traditional algorithms and ensures reliable and adaptive telemedicine applications with better clustering of biomedical documents and health records.

Keywords: telemedicine; health records; biomedical document clustering; semantic smoothing; TF-IGM; chicken swarm optimisation; CSO; dimension reduction; ontology; concept mapping; modified n-grams; PubMed.

DOI: 10.1504/IJENM.2019.103158

International Journal of Enterprise Network Management, 2019 Vol.10 No.3/4, pp.305 - 328

Received: 16 Oct 2018
Accepted: 24 Mar 2019
Published online: 21 Oct 2019 *

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Title: Chicken swarm optimisation based clustering of biomedical documents and health records to improve telemedicine applications

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