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Srividya Sivasankar
Amrita Vishwa Vidyapeetha
Computer Science & IT Electrical & Electronics Engineering

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Feature Reduction in Clinical Data Classification using Augmented Genetic Algorithm Srividya Sivasankar; Sruthi Nair; M.V Judy
International Journal of Electrical and Computer Engineering (IJECE) Vol 5, No 6: December 2015
Publisher : Institute of Advanced Engineering and Science

Show Abstract | Download Original | Original Source | Check in Google Scholar | Full PDF (420.903 KB) | DOI: 10.11591/ijece.v5i6.pp1516-1524

Abstract

In clinical data, we have a large set of diagnostic feature and recorded details of patients for certain diseases. In a clinical environment a doctor reaches a treatment decision based on his theoretical knowledge, information attained from patients, and the clinical reports of the patient. It is very difficult to work with huge data in machine learning; hence to reduce the data, feature reduction is applied. Feature reduction has gained interest in many research areas which deals with machine learning and data mining, because it enhances the classifiers in terms of faster execution, cost-effectiveness, and accuracy. Using feature reduction we intend to find the relevant features of the data set. In this paper, we have analyzed Modified GA (MGA), PCA and combination of PCA and Modified Genetic algorithm for feature reduction. We have found that correctly classified rate of combination of PCA and Modified Genetic algorithm higher compared to other feature reduction method.
Co-Authors M.V Judy Sruthi Nair