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Title of Journal: Neural Comput Applic

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Abbravation: Neural Computing and Applications

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Springer London

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DOI

10.1002/mus.20665

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ISSN

1433-3058

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Artificial neural network model for effective canc

Authors: Ashok Kumar Dwivedi
Publish Date: 2016/11/12
Volume: 29, Issue: 12, Pages: 1545-1554
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Abstract

Microarray gene expression profile shall be exploited for the efficient and effective classification of cancers This is a computationally challenging task because of large quantity of genes and relatively small amount of experiments in gene expression data The repercussion of this work is to devise a framework of techniques based on supervised machine learning for discrimination of acute lymphoblastic leukemia and acute myeloid leukemia using microarray gene expression profiles Artificial neural network ANN technique was employed for this classification Moreover ANN was compared with other five machine learning techniques These methods were assessed on eight different classification performance measures This article reports a significant classification accuracy of 98 using ANN with no error in identification of acute lymphoblastic leukemia and only one error in identification of acute myeloid leukemia on tenfold crossvalidation and leaveoneout approach Furthermore models were validated on independent test data and all samples were correctly classified

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