TY  - JOUR
T1  - Recursive Feature Elimination and Gravitational Search Algorithm for
Classification of Medical Data
AU - Penchala Prasad, P. AU - Francis, F. Sagayaraj AU - Zahoor-Ul-Huq, S. 
JO  - Journal of Engineering and Applied Sciences
VL  - 14
IS  - 23
SP  - 8828
EP  - 8834
PY  - 2019
DA  - 2001/08/19
SN  - 1816-949x
DO  - jeasci.2019.8828.8834
UR  - https://makhillpublications.co/view-article.php?doi=jeasci.2019.8828.8834
KW  - Feature selection
KW  -gravitational search algorithm
KW  -medical data classification
KW  -recursive feature
elimination and UCI dataset
KW  -Radial Basis Function (RBF)
KW  -UCI
AB  - Medical data classification is the challenging task due to noisy data or missing data are present in
the dataset. The feature selection techniques play the important part in the classification process. The more
relevant features help to provide the efficient classification of medical data which is essential for the disease
detection. In this research, Recursive Feature Elimination with the Gravitational Search Algorithm (RFE-GSA)
is proposed for efficient classification of the data. The Recursive Feature Elimination (RFE) method helps to
remove the irrelevant features from the medical data and rank them in order of importance that helps to reduce
the computation cost of the proposed method. The ranked features from the RFE are given as input to the GSA
which select the feature for the classification. The GSA is fast convergence and that helps to find the relevant
features in the data. The features selected from the RFE-GSA is provided as input to the Radial Basis Function
(RBF) for the classification. The performance of the RFE-GSA method is high compared to the other existing
method. The proposed RFE-GSA method has the accuracy of the 98.24% in the breast cancer dataset in UCI
dataset and the state-of-art method has achieved the accuracy of 96.87%.
ER  -