Oral cancer prediction using gene expression profiling and machine learning
Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support v...
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Research India Publications
2017
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iium-604382017-12-27T07:13:32Z http://irep.iium.edu.my/60438/ Oral cancer prediction using gene expression profiling and machine learning K. Shams, Wafaa Htike@Muhammad Yusof, Zaw Zaw T Technology (General) Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back propagation and deep neural network (DNN). Fisher discriminate analysis was used to select relevant features from the gene expression array. The results show high accuracy (96%) using DNN and 94% accuracy using SVM and MLP with one sample cross validation. Furthermore, we achieved the same results using 10-fold cross validation. Research India Publications 2017-08 Article PeerReviewed application/pdf en http://irep.iium.edu.my/60438/1/23_58091-IJAER%20ok%204893-4898.pdf application/pdf en http://irep.iium.edu.my/60438/7/60438-Oral%20cancer%20prediction%20using%20gene%20expression%20profiling_SCOPUS.pdf K. Shams, Wafaa and Htike@Muhammad Yusof, Zaw Zaw (2017) Oral cancer prediction using gene expression profiling and machine learning. International Journal of Applied Engineering Research, 12 (15). pp. 4893-4898. ISSN 0973-4562 E-ISSN 0973-9769 https://www.ripublication.com/ijaer17/ijaerv12n15_%20(23).pdf |
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T Technology (General) K. Shams, Wafaa Htike@Muhammad Yusof, Zaw Zaw Oral cancer prediction using gene expression profiling and machine learning |
description |
Oral premalignant lesion (OPL) patients have a high risk of developing oral cancer. In this study we investigate using machine learning techniques with gene expression profiling to predict the possibility of oral cancer development in OPL patients. Four classification techniques were used: support vector machine (SVM), Regularized Least Squares (RLS), multi-layer perceptron (MLP) with back propagation and deep neural network (DNN). Fisher discriminate analysis was used to select relevant features from the gene expression array. The results show high accuracy (96%) using DNN and 94% accuracy using SVM and MLP with one sample cross validation. Furthermore, we achieved the same results using 10-fold cross validation. |
format |
Article |
author |
K. Shams, Wafaa Htike@Muhammad Yusof, Zaw Zaw |
author_facet |
K. Shams, Wafaa Htike@Muhammad Yusof, Zaw Zaw |
author_sort |
K. Shams, Wafaa |
title |
Oral cancer prediction using gene expression profiling and machine learning |
title_short |
Oral cancer prediction using gene expression profiling and machine learning |
title_full |
Oral cancer prediction using gene expression profiling and machine learning |
title_fullStr |
Oral cancer prediction using gene expression profiling and machine learning |
title_full_unstemmed |
Oral cancer prediction using gene expression profiling and machine learning |
title_sort |
oral cancer prediction using gene expression profiling and machine learning |
publisher |
Research India Publications |
publishDate |
2017 |
url |
http://irep.iium.edu.my/60438/ http://irep.iium.edu.my/60438/ http://irep.iium.edu.my/60438/1/23_58091-IJAER%20ok%204893-4898.pdf http://irep.iium.edu.my/60438/7/60438-Oral%20cancer%20prediction%20using%20gene%20expression%20profiling_SCOPUS.pdf |
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2023-09-18T21:25:41Z |
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2023-09-18T21:25:41Z |
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