Share This Article:

Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique

Abstract Full-Text HTML Download Download as PDF (Size:313KB) PP. 682-686
DOI: 10.4236/jsea.2012.59081    7,669 Downloads   17,321 Views   Citations


Software technology based on reuse is identified as a process of designing software for the reuse purpose. The software reuse is a process in which the existing software is used to build new software. A metric is a quantitative indicator of an attribute of an item/thing. Reusability is the likelihood for a segment of source code that can be used again to add new functionalities with slight or no modification. A lot of research has been projected using reusability in reducing code, domain, requirements, design etc., but very little work is reported using software reuse in medical domain. An attempt is made to bridge the gap in this direction, using the concepts of clustering and classifying the data based on the distance measures. In this paper cardiologic database is considered for study. The developed model will be useful for Doctors or Para-medics to find out the patient’s level in the cardiologic disease, deduce the medicines required in seconds and propose them to the patient. In order to measure the reusability K-means clustering algorithm is used.

Conflicts of Interest

The authors declare no conflicts of interest.

Cite this paper

M. Sridhar, Y. Srinivas and M. Krishna Prasad, "Software Reuse in Cardiology Related Medical Database Using K-Means Clustering Technique," Journal of Software Engineering and Applications, Vol. 5 No. 9, 2012, pp. 682-686. doi: 10.4236/jsea.2012.59081.


[1] Heart Attack Dataset, 2012.
[2] A. M. Spalter and A. van Dam, “Problems with Using Components in Educational Software,” Computers & Graphics, Vol. 27, No. 3, 2003, pp. 329-337. doi:10.1016/S0097-8493(03)00027-X
[3] Press Release by Delta Heart Centre, Ludhiana, 2012.
[5] Interview with Dr. V. Rama Narasimham, Senior Cardiology Specialist, Visakhapatnam.
[7] R. Gibbons, G. Balady, J. T. Bricker, B. Chaitman, G. Fletcher, V. Froelicher, D. Mark, B. McCallister, et al., “ACA/AHA Guideline Update for Exercise testing: A Summary Article,” Journal of the American College of Cardiology, Vol. 40, No. 8, 2002, pp. 1531-1540. doi:10.1016/S0735-1097(02)02164-2
[8] B. Delibasic, K. Kirchner, et al., “Reusable Components for Partitioning Clustering Algorithms,” Artificial Intelligence Review, Vol. 32, No. 1-4, 2009, pp. 59-75. doi:10.1007/s10462-009-9133-6
[9] C. Ordonez, “Clustering Binary Data Streams with K-Means,” DMKD’03, San Diego, 2003.
[10] G. Casella and R. L. Berger, “Statistical Inference,” 2nd Edition, Duxbury Press, Duxbury, 2001.
[11] R. Godin, G. Mineau, et al., “Applying Concept Formation Methods to Software Reuse,” International Journal of Software Engineering and Knowledge Engineering, Vol. 5, No. 1, 1995, pp. 119-142. doi:10.1142/S0218194095000071

comments powered by Disqus

Copyright © 2018 by authors and Scientific Research Publishing Inc.

Creative Commons License

This work and the related PDF file are licensed under a Creative Commons Attribution 4.0 International License.