Title: Heart diseases data classification using group search optimisation with artificial neural network approach
Authors: M. Babu; N. Ramaraj; S.P. Rajagopalan
Addresses: Department of Computer Science and Engineering GKM College of Engineering and Technology, Chennai, Tamil Nadu, India ' Electrical and Electronics Engineering Department, Vignan's University, Guntur, Andhra Pradesh, India ' Computer Science and Engineering Department, GKM College of Engineering and Technology, Chennai, Tamil Nadu, India
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.
Keywords: artificial neural network; ANN; classification process; group search optimisation; GSO; weight and heart diseases data.
International Journal of Business Intelligence and Data Mining, 2017 Vol.12 No.3, pp.257 - 273
Received: 18 Aug 2016
Accepted: 10 Dec 2016
Published online: 10 Jul 2017 *