Year: 2017
Type: conference-paper
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.
Source: Procedia Computer Science
Authors: Sushama Nagpal, Sanchit Arora, Sangeeta Dey, Shreya
Institution: Netaji Subhas University of Technology
Language: English
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W2765424924