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Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels
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 Title & Authors
Evaluation of Machine Learning Algorithm Utilization for Lung Cancer Classification Based on Gene Expression Levels
Podolsky, Maxim D; Barchuk, Anton A; Kuznetcov, Vladimir I; Gusarova, Natalia F; Gaidukov, Vadim S; Tarakanov, Segrey A;
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 Abstract
Background: Lung cancer remains one of the most common cancers in the world, both in terms of new cases (about 13% of total per year) and deaths (nearly one cancer death in five), because of the high case fatality. Errors in lung cancer type or malignant growth determination lead to degraded treatment efficacy, because anticancer strategy depends on tumor morphology. Materials and Methods: We have made an attempt to evaluate effectiveness of machine learning algorithms in the task of lung cancer classification based on gene expression levels. We processed four publicly available data sets. The Dana-Farber Cancer Institute data set contains 203 samples and the task was to classify four cancer types and sound tissue samples. With the University of Michigan data set of 96 samples, the task was to execute a binary classification of adenocarcinoma and non-neoplastic tissues. The University of Toronto data set contains 39 samples and the task was to detect recurrence, while with the Brigham and Women`s Hospital data set of 181 samples it was to make a binary classification of malignant pleural mesothelioma and adenocarcinoma. We used the k-nearest neighbor algorithm (k
 Keywords
Computer aided diagnosis;lung cancer;ROC curve;data set;classifiers;gene expression;
 Language
English
 Cited by
1.
Evaluating Various Lung Cancer Nodule Detection Techniques—A Comparative Study, Journal of Testing and Evaluation, 2017, 46, 2, 20160410  crossref(new windwow)
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