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A Hybrid Model for Mining and Classification of Gene Expression Pattern for Detecting Neurodegenerative Disorder

  • S. GeeithaEmail author
  • M. Thangamani
Conference paper
Part of the Advances in Intelligent Systems and Computing book series (AISC, volume 713)

Abstract

The exploration of gene expression data leads to various discovery of diseases in the human life. This research classifies gene expression pattern and detects the discriminative genes associated with neurodegenerative diseases by implementing the Naive Bayesian (NB) network model based on particle swarm optimization (PSO) techniques to reduce the disease dimension. Artificial neural network (ANN) is a traditional approach used to classify the disease type and produces either failure or non-failure based on the disease features. The integration of artificial neural network (ANN) and Bayesian logistic regression (BLR) model has been developed to pre-select the gene sample for feature selection, and those selected genes are then used to construct the ANN model. This hybrid model is mainly employed to reduce the time in gene classification and uncovers the diseased gene expression pattern that helps in selecting the victim genes for early detection of diseases in the medical era.

Keywords

Data mining Gene expression pattern Neurodegenerative disorder Naive Bayesian network model Particle swarm optimization technique Artificial neural network Bayesian logistic regression 

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Copyright information

© Springer Nature Singapore Pte Ltd. 2019

Authors and Affiliations

  1. 1.Kongu Engineering CollegePerundurai, ErodeIndia

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