Advances in Artificial Neural Systems
Volume 2015 (2015), Article ID 265637, 10 pages
Research Article

Hybrid Feature Selection Based Weighted Least Squares Twin Support Vector Machine Approach for Diagnosing Breast Cancer, Hepatitis, and Diabetes

Indian Institute of Information Technology, Allahabad 211012, India

Received 30 September 2014; Accepted 23 December 2014

Academic Editor: Chao-Ton Su

Copyright © 2015 Divya Tomar and Sonali Agarwal. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


There is a necessity for analysis of a large amount of data in many fields such as healthcare, business, industries, and agriculture. Therefore, the need of the feature selection (FS) technique for the researchers is quite evident in many fields of science, especially in computer science. Furthermore, an effective FS technique that is best suited to a particular learning algorithm is of great help for the researchers. Hence, this paper proposes a hybrid feature selection (HFS) based efficient disease diagnostic model for Breast Cancer, Hepatitis, and Diabetes. A HFS is an efficient method that combines the positive aspects of both Filter and Wrapper FS approaches. The proposed model adopts weighted least squares twin support vector machine (WLSTSVM) as a classification approach, sequential forward selection (SFS) as a search strategy, and correlation feature selection (CFS) to evaluate the importance of each feature. This model not only selects relevant feature subset but also efficiently deals with the data imbalance problem. The effectiveness of the HFS based WLSTSVM approach is examined on three well-known disease datasets taken from UCI repository with the help of predictive accuracy, sensitivity, specificity, and geometric mean. The experiment confirms that our proposed HFS based WLSTSVM disease diagnostic model can result in positive outcomes.