Please use this identifier to cite or link to this item: https://hdl.handle.net/10321/1574
Title: Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features
Authors: Adetiba, Emmanuel 
Olugbara, Oludayo O. 
Issue Date: 2015
Publisher: Hindawi Publishing Corporation
Source: Adetiba, E. and Olugbara, O.O. 2015. Lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features. The Scientific World Journal. 2015: 1-17. http://dx.doi.org/10.1155/2015/786013.
Journal: The scientific world journal (Print) 
Abstract: 
This paper reports an experimental comparison of artificial neural network (ANN) and support vector machine (SVM) ensembles and their “nonensemble” variants for lung cancer prediction. These machine learning classifiers were trained to predict lung cancer using samples of patient nucleotides with mutations in the epidermal growth factor receptor, Kirsten rat sarcoma viral oncogene, and tumor suppressor p53 genomes collected as biomarkers from the IGDB.NSCLC corpus. The Voss DNA encoding was used to map the nucleotide sequences of mutated and normal genomes to obtain the equivalent numerical genomic sequences for training the selected classifiers. The histogram of oriented gradient (HOG) and local binary pattern (LBP) state-of-the-art feature extraction schemes were applied to extract representative genomic features from the encoded sequences of nucleotides. The ANN ensemble and HOG best fit the training dataset of this study with an accuracy of 95.90% and mean square error of 0.0159. The result of the ANN ensemble and HOG genomic features is promising for automated screening and early detection of lung cancer. This will hopefully assist pathologists in administering targeted molecular therapy and offering counsel to early stage lung cancer patients and persons in at risk populations.
URI: http://hdl.handle.net/10321/1574
ISSN: 2356-6140
DOI: http://dx.doi.org/10.1155/2015/786013
Appears in Collections:Research Publications (Accounting and Informatics)

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