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Data Adaptive Rule-based Classification System for Alzheimer Classification | OMICS International | Abstract
ISSN: 0974-7230

Journal of Computer Science & Systems Biology
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Research Article

Data Adaptive Rule-based Classification System for Alzheimer Classification

Mohit Jain1*, Prerna Dua2, Sumeet Dua1 and Walter J Lukiw3

1Department of Computer Science, Louisiana Tech University, Ruston, LA 71270, USA

2Department of Health Informatics and Information Management, Louisiana Tech University, Ruston, LA 71270, USA

3Neuroscience Centre of Excellence, Louisiana State University Health Sciences Centre, New Orleans, LA 70112, USA

*Corresponding Author:
Mohit Jain
Department of Computer Science
Louisiana Tech University
Ruston, LA 71270, USA
E-mail: [email protected]

Received date: June 28, 2013; Accepted date: August 28, 2013; Published date: September 07, 2013

Citation: Jain M, Dua P, Dua S, Lukiw WJ (2013) Data Adaptive Rule-based Classification System for Alzheimer Classification. J Comput Sci Syst Biol 6:291-297 doi:10.4172/jcsb.1000124

Copyright: © 2013 Jain M, et al. This is an open-access article distributed under the terms of the Creative Commons Attribution License,which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.


Microarrays have already produced huge amounts of valuable genetic data that is challenging to analyse due to its high dimensionality and complexity. An inherent problem with the microarray data which is characteristic of diseases such as Alzheimer’s is that they face computational complexity due to the sparseness of the points within the data, which affect both the accuracy and the efficiency of supervised learning methods. This paper proposes a data-adaptive rule-based classification system for Alzheimer’s disease classification that generates relevant rules by finding adaptive partitions using gradient-based partitioning of the data. The adaptive partitions are generated from the histogram by analyzing Tuple Tests following which efficient and relevant rules are discovered that assist in classifying the new data correctly. The proposed approach has been compared with other rule-based and machine learning classifiers, and detailed results and discussion of the experiments are presented to demonstrate comparative analysis and the efficacy of the results.


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