A Hybrid Filter-Wrapper Attribute Reduction Approach For Fetal Risk Anticipation

Abstract

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.