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Computational and Mathematical Methods in Medicine
Volume 2015, Article ID 676129, 11 pages
Research Article

Ensemble Merit Merge Feature Selection for Enhanced Multinomial Classification in Alzheimer’s Dementia

1Department of Computer Science, Lady Doak College, Madurai, Tamil Nadu 625002, India
2The Alzheimer’s Disease Neuroimaging Initiative, San Diego, CA 92093-0949, USA
3Department of Computer Science, TBAK College, Kilakarai, Tamil Nadu 623517, India
4Department of Computer Applications, Karunya University, Coimbatore, Tamil Nadu 641114, India

Received 8 February 2015; Revised 8 May 2015; Accepted 18 May 2015

Academic Editor: Maria N. D. S. Cordeiro

Copyright © 2015 T. R. Sivapriya et al. 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.


The objective of this study is to develop an ensemble classifier with Merit Merge feature selection that will enhance efficiency of classification in a multivariate multiclass medical data for effective disease diagnostics. The large volumes of features extracted from brain Magnetic Resonance Images and neuropsychological tests for diagnosis lead to more complexity in classification procedures. A higher level of objectivity than what readers have is needed to produce reliable dementia diagnostic techniques. Ensemble approach which is trained with features selected from multiple biomarkers facilitated accurate classification when compared with conventional classification techniques. Ensemble approach for feature selection is experimented with classifiers like Naïve Bayes, Random forest, Support Vector Machine, and C4.5. Feature search is done with Particle Swarm Optimisation to retrieve the subset of features for further selection with the ensemble classifier. Features selected by the proposed C4.5 ensemble classifier with Particle Swarm Optimisation search, coupled with Merit Merge technique (CPEMM), outperformed bagging feature selection of SVM, NB, and Random forest classifiers. The proposed CPEMM feature selection found the best subset of features that efficiently discriminated normal individuals and patients affected with Mild Cognitive Impairment and Alzheimer’s Dementia with 98.7% accuracy.