Heart diseases data classification using group search optimisation with artificial neural network approach Online publication date: Mon, 10-Jul-2017
by M. Babu; N. Ramaraj; S.P. Rajagopalan
International Journal of Business Intelligence and Data Mining (IJBIDM), Vol. 12, No. 3, 2017
Abstract: Cardiovascular disease remains the biggest cause of deaths worldwide and the heart disease prediction at the early stage is very important. The classification problem of assigning several observations into different disjoint groups plays an important role in business decision making and many other areas. A heart disease dataset is analysed using neural network approach. The main aim of data mining is to find relationships in data and to predict outcomes. Classification is one of the important data mining techniques for classifying given set of input data. Many real world problems in various fields such as business, science, industry and medicine, can be solved by using classification approach. For the better classification and improve the accuracy, optimisation technique is used. To optimise the weight of the ANN structure, GSO technique is used. From the classification results process, the maximum accuracy is 82.23% in heart diseases database classification process.
Existing subscribers:
Go to Inderscience Online Journals to access the Full Text of this article.
If you are not a subscriber and you just want to read the full contents of this article, buy online access here.Complimentary Subscribers, Editors or Members of the Editorial Board of the International Journal of Business Intelligence and Data Mining (IJBIDM):
Login with your Inderscience username and password:
Want to subscribe?
A subscription gives you complete access to all articles in the current issue, as well as to all articles in the previous three years (where applicable). See our Orders page to subscribe.
If you still need assistance, please email subs@inderscience.com
Our Blog 
Follow us on Twitter 
Visit us on Facebook