Feature Selection using Gravitational Search Algorithm for Biomedical Data

Abstract

Analysis of medical data for disease prediction requires efficient feature selection techniques, as the data contains a large number of features. Researchers have used evolutionary computation (EC) techniques like genetic algorithms, particle swarm optimization etc. for FS and have found them to be faster than traditional techniques. We have explored a relatively new EC technique called gravitational search algorithm (GSA) for feature selection in medical datasets. This wrapper based method, that we have employed, using GSA and k-nearest neighbors reduces the number of features by an average of 66% and considerably improves the accuracy of prediction.