Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features
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 mu...
Saved in:
| Main Authors: | , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Wiley
2015-01-01
|
| Series: | The Scientific World Journal |
| Online Access: | http://dx.doi.org/10.1155/2015/786013 |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1832554524056748032 |
|---|---|
| author | Emmanuel Adetiba Oludayo O. Olugbara |
| author_facet | Emmanuel Adetiba Oludayo O. Olugbara |
| author_sort | Emmanuel Adetiba |
| collection | DOAJ |
| description | 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. |
| format | Article |
| id | doaj-art-d0adf72a6c4f4508884affd7399016f9 |
| institution | Kabale University |
| issn | 2356-6140 1537-744X |
| language | English |
| publishDate | 2015-01-01 |
| publisher | Wiley |
| record_format | Article |
| series | The Scientific World Journal |
| spelling | doaj-art-d0adf72a6c4f4508884affd7399016f92025-02-03T05:51:22ZengWileyThe Scientific World Journal2356-61401537-744X2015-01-01201510.1155/2015/786013786013Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic FeaturesEmmanuel Adetiba0Oludayo O. Olugbara1ICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban 4000, South AfricaICT and Society Research Group, Durban University of Technology, P.O. Box 1334, Durban 4000, South AfricaThis 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.http://dx.doi.org/10.1155/2015/786013 |
| spellingShingle | Emmanuel Adetiba Oludayo O. Olugbara Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features The Scientific World Journal |
| title | Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features |
| title_full | Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features |
| title_fullStr | Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features |
| title_full_unstemmed | Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features |
| title_short | Lung Cancer Prediction Using Neural Network Ensemble with Histogram of Oriented Gradient Genomic Features |
| title_sort | lung cancer prediction using neural network ensemble with histogram of oriented gradient genomic features |
| url | http://dx.doi.org/10.1155/2015/786013 |
| work_keys_str_mv | AT emmanueladetiba lungcancerpredictionusingneuralnetworkensemblewithhistogramoforientedgradientgenomicfeatures AT oludayooolugbara lungcancerpredictionusingneuralnetworkensemblewithhistogramoforientedgradientgenomicfeatures |