Open Access   Article

Application of Big Data Tools and Techniques in Prediction of Heart Diseases

R. Sharmila1 , S. Chellammal2

Section:Research Paper, Product Type: Journal Paper
Volume-06 , Issue-11 , Page no. 14-16, Dec-2018

Online published on Dec 31, 2018

Copyright © R. Sharmila, S. Chellammal . This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

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IEEE Style Citation: R. Sharmila, S. Chellammal, “Application of Big Data Tools and Techniques in Prediction of Heart Diseases”, International Journal of Computer Sciences and Engineering, Vol.06, Issue.11, pp.14-16, 2018.

MLA Style Citation: R. Sharmila, S. Chellammal "Application of Big Data Tools and Techniques in Prediction of Heart Diseases." International Journal of Computer Sciences and Engineering 06.11 (2018): 14-16.

APA Style Citation: R. Sharmila, S. Chellammal, (2018). Application of Big Data Tools and Techniques in Prediction of Heart Diseases. International Journal of Computer Sciences and Engineering, 06(11), 14-16.



Heart disease is one of the major causes of death in human life. But, prediction of heart disease with desired accuracy is difficult due to many reasons. For example, the database of heart disease is being archived for many years with huge volume which are too large for traditional systems to process. Also, the clinical reports and medical tests related to heart disease produce in a variety of formats such as text, images, sound etc which are not effectively handled by traditional database systems. Nowadays data mining algorithms and big data technologies play crucial role in the prediction of heart diseases. In addition, big data techniques are useful in finding the patterns of heart disease in its early stage. Analysis of heart disease data can be done on big scale using Hadoop, R and MapReduce. In our previous paper, we proposed a conceptual approach for prediction of heart diseases with Support Vector Machine (SVM) in parallel programming fashion. In relation to our previous work, in this paper, an investigation is done in finding the applicability of different big data tools and technologies for prediction of heart diseases.

Key-Words / Index Term

Big data tools, Hadoop, Map Reduce, prediction of diseases


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