Oppositional Cuckoo Search Optimization based Clustering with Classification Model for Big Data Analytics in Healthcare Environment

T. Gayathri; D. Lalitha Bhaskari

doi:10.6180/jase.202208_25(4).0019

Oppositional Cuckoo Search Optimization based Clustering with Classification Model for Big Data Analytics in Healthcare Environment

Computer Science and Information Engineering

T. Gayathri This email address is being protected from spambots. You need JavaScript enabled to view it.¹ and D. Lalitha Bhaskari¹

¹Research Scholar, Department of CS&SE, AUCE (A), Visakhapatnam, Andhra Pradesh, India

Received: July 17, 2021
Accepted: September 18, 2021
Publication Date: December 6, 2021

Copyright The Author(s). This is an open access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are cited.

Download Citation: ||https://doi.org/10.6180/jase.202208_25(4).0019

ABSTRACT

Big data in healthcare defines a massive quantity of healthcare data accumulated from massive sources like electronic health records (EHR), medical imaging, genomic sequence, pharmacological research, wearable, and medical gadgets, etc. One of the data mining approaches commonly employed to classify big data is the MapReduce model. Data clustering, a significant data mining technique has been extensively investigated in the recent years in handling the diversity in data and various sets of application necessities. In this view, this paper develops an enhanced metaheuristic algorithm based clustering and classification model with MapReduce (EMACC-MR) framework for big data environment. The presented EMACC-MR model involves an oppositional cuckoo search optimization algorithm (OCSOA) with Density-Based Spatial Clustering of Applications with Noise (DBSCAN) and wavelet kernel extreme learning machine (WKELM) model based on the classification model. The inclusion of oppositional based learning (OBL) concept helps to improve the convergence rate of the CSOA. For handling big data, Hadoop MapReduce environment is employed. The proposed OCSOA model improves the clustering quality and MapReduce architecture to cope with the large-scale dataset. For validating the experimental analysis of the proposed model, two benchmark datasets namely Activity recognition and diabetes datasets are used. The simulation outcomes confirmed that the presented model outperforms the compared methods in terms of several evaluation parameters.

Keywords: Big data analytics, Neural Network, Clustering, Classification process, Metaheuristics, MapReduce

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