*Assistant Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
**Associate Professor, Department of Computer Science and Engineering, Manonmaniam Sundaranar University, Tirunelveli, India
***Associate Professor, Department of Electrical & Electronics Engineering, PSN College of Engineering & Technology, Tirunelveli, India
Online published on 14 February, 2017.
In the present days, use of computers has permeated in archiving and analysing vast amount of medical data. Data mining techniques are widely used on these data to excerpt the information and by which an accurate and easy prediction of diseases has become possible. In this paper, Support Vector Machine (SVM) is used to analyse the Cardiotocogram (CTG) data for the prediction fetal risk. A hybrid method which combines Information Gain (IG) and Opposition based firefly algorithm (OBFA), is proposed in this paper to excerpt the most relevant features/attributes which will improve the classification performance of SVM. The results prove that the proposed hybrid method performs better than the other existing methods.
CTG, SVM Classifier, Feature Selection, Information Gain, Opposition-based firefly algorithm
