Classification of SNPs for Obesity Analysis using FARNeM Modelling

Ong, Phaik Ling and Choo, Yun Huoy and Nurul A., Emran (2013) Classification of SNPs for Obesity Analysis using FARNeM Modelling. In: ISDA 2013, Dec. 8-10, 2013, UPM, Malaysia.

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Abstract

Recent research found that genetics plays an important role in obesity risk analysis besides life styles. Many literatures are focusing on analyzing the effect of Single Nucleotide Polymorphism (SNPs) towards obesity to facilitate personalized medication. However, SNPs data are normally large and noisy, which affects the accuracy and computational complexity on data processing and analysis. Therefore, efficient data reduction is essential to yield better analysis results and reduce computational complexity in the experimentations. In this paper, we investigated feature selection process in obesity related SPNs analysis using Forward attribute reduction based on neighbourhood rough set model (FARNeM). The experimental results were compared against Correlation Feature Selection (CFS) method and ReliefF method. Classification accuracy, sensitivity, specificity, positive predictive value and negative predictive value were chosen to assess the performance of the comparison methods on error rate and validated by paired-sample T-test. FARNeM has outperformed other comparison techniques by having three highest performances which are specificity, positive predictive value and negative predictive value. But, FARNeM did not achieve good reduction rate when applied to the experimental data set. However, the overall analysis showed that, it is encouraging to include feature selection process before the learning algorithms.

Item Type: Conference or Workshop Item (Paper)
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Divisions: Faculty of Information and Communication Technology > Department of Industrial Computing
Depositing User: Dr. Yun-Huoy Choo
Date Deposited: 24 Mar 2014 01:57
Last Modified: 21 Nov 2016 08:03
URI: http://eprints.utem.edu.my/id/eprint/11906
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