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General Information
Editor-in-chief
Dr. Lin Huang
Metropolitan State University of Denver, USA
It's my honor to take on the position of editor in chief of IJMLC. We encourage authors to submit papers concerning any branch of machine learning and computing.
IJMLC 2013 Vol.3(2): 181-185 ISSN: 2010-3700
DOI: 10.7763/IJMLC.2013.V3.298

Automated Classification of Dementia Using PSO based Least Square Support Vector Machine

T. R. Sivapriya, A. R. Nadira Banu Kamal, and V. Thavavel
Abstract—Machine learning techniques are widely used now for neuro-imaging based diagnosis. These methods yield fully automated clinical decisions, unbiased by variable radiological expertise. This research paper compares and evaluates the performance and reliability of conventional Least Square Support Vector Machine (LSSVM) with that of Particle Swarm Optimization (PSO) based LSSVM in the diagnosis of dementia. The manual interpretation of large volume of brain MRI and cognitive measures may lead to incomplete diagnosis. The PSO-LSSVM approach is trained with multiple biomarkers to facilitate effective, accurate classification which is a requirement of the hour. Wavelet based texture features and multiple biomarkers are fed as input to the classifier. PSO-LSSVM yields 98% accurate results and outperforms LSSVM classifier in terms of sensitivity, specificity and accuracy in this analysis

Index Terms—Classification, dementia, least square support vector machine, particle swarm optimization.

T. R. Sivapriya is with the Department of Computer Science, Lady Doak College, Madurai, Tamilnadu, India. (e-mail:spriya.tr@gmail.com).
A. R. Nadira Banu Kamal is with the Department of MCA, T.B.A.K. College for Women, Kilakarai, Ramanathapuram,Tamilnadu, India (e-mail: nadira_kamal@hotmail.com).
V.Thavavel is with the Department of MCA, Karunya University, Coimbatore, Tamilnadu, India (e-mail: vthavavel@karunya.edu).

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Cite:T. R. Sivapriya, A. R. Nadira Banu Kamal, and V. Thavavel, "Automated Classification of Dementia Using PSO based Least Square Support Vector Machine," International Journal of Machine Learning and Computing vol. 3, no. 2, pp. 181-185, 2013.

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