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Cellular Automata Based Model for E-Healthcare Data Analysis

Cellular Automata Based Model for E-Healthcare Data Analysis

Hakam Singh, Yugal Kumar
Copyright: © 2019 |Volume: 10 |Issue: 3 |Pages: 18
ISSN: 1947-8186|EISSN: 1947-8194|EISBN13: 9781522566625|DOI: 10.4018/IJISMD.2019070101
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MLA

Singh, Hakam, and Yugal Kumar. "Cellular Automata Based Model for E-Healthcare Data Analysis." IJISMD vol.10, no.3 2019: pp.1-18. http://doi.org/10.4018/IJISMD.2019070101

APA

Singh, H. & Kumar, Y. (2019). Cellular Automata Based Model for E-Healthcare Data Analysis. International Journal of Information System Modeling and Design (IJISMD), 10(3), 1-18. http://doi.org/10.4018/IJISMD.2019070101

Chicago

Singh, Hakam, and Yugal Kumar. "Cellular Automata Based Model for E-Healthcare Data Analysis," International Journal of Information System Modeling and Design (IJISMD) 10, no.3: 1-18. http://doi.org/10.4018/IJISMD.2019070101

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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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