Cellular Automata Based Model for E-Healthcare Data Analysis

Cellular Automata Based Model for E-Healthcare Data Analysis

Hakam Singh (Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India) and Yugal Kumar (Department of Computer Science and Engineering, Jaypee University of Information Technology Waknaghat, Solan, Himachal Pradesh, India)
Copyright: © 2019 |Pages: 18
DOI: 10.4018/IJISMD.2019070101

Abstract

E-healthcare is warm area of research and a number of algorithms have been applied to classify healthcare data. In the healthcare field, a large amount of clinical data is generated through MRI, CT scans, and other diagnostic tools. Healthcare analytics are used to analyze the clinical data of patient records, disease diagnosis, cost, hospital management, etc. Analytical techniques and data visualization are used to get the real time information. Further, this information can be used for decision making. Also, this information is useful for the better treatment of patients. In this work, an improved big bang-big crunch (BB-BC) based clustering algorithm is applied to analyze healthcare data. Cluster analysis is an important task in the field of data analysis and can be used to understand the organization of data. In this work, two healthcare datasets, CMC and cancer, are used and the proposed algorithm obtains better results when compared to MEBB-BC, BB-BC, GA, PSO and K-means algorithms. The performance of the improved BB-BC algorithm is also examined against benchmark clustering datasets. The simulation results showed that proposed algorithm improves the clustering results significantly when compared to other algorithms.
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1. Introduction

Cluster analysis is an unsupervised machine learning approach, which is used to partition dataset into different subsets (Kushwaha et al., 2018; Kant & Ansari, 2016). The data items associated with same subset are more similar in nature than other (Aggarwal & Reddy, 2013). Broadly, clustering methods are divided into two categories: hierarchical and partitional clustering (Xu & Wunsch, 2005). Hierarchal clustering is also divided into two groups i.e. agglomerative and divisive. The agglomerative method works in bottom up fashion, whereas, divisive method works in top down manner. In partitional clustering method, data is divided into several disjoint clusters that are optimal in nature. It is observed that large number of meta-heuristic algorithms has been reported in literature to find optimal results for clustering problems (Shelokar et al., 2004; Xiao et al., 2010; Cura, 2012; Taherdangkoo et al., 2013; Kumar & Sahoo, 2014; Bahrololoum et al., 2015). It is also seen that several improved versions of these meta-heuristic algorithms are also presented to solve clustering problems effectively (Chuang et al., 2011; Jiang & Wang, 2014). Moreover, some hybrid versions of meta-heuristic algorithms also developed to determine effective clustering results as well as to overcome shortcomings of existing clustering algorithms (Abualigah et al., 2017; Sheng et al., 2010; Huang et al., 2013; Yan et al., 2012; Bouyer & Hatamlou, 2018; Kumar & Singh, 2018; Wang et al., 2016). It is noticed that several issues are associated with the performance of clustering algorithms like local optima, population initialization and convergence speed etc. (Cao et al., 2009; Kang et al., 2016; Kumar & Singh, 2019). To overcome these issues, researchers either hybridized the existing algorithms or developed new algorithm to obtain better clustering results.

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