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http://yadda.icm.edu.pl:80/baztech/element/bwmeta1.element.baztech-d317c4e6-167c-451f-ab33-8b860e1ef5e0

Czasopismo

Biocybernetics and Biomedical Engineering

Tytuł artykułu

A multi-layered incremental feature selection algorithm for adjuvant chemotherapy effectiveness/futileness assessment in non-small cell lung cancer

Autorzy Naftchali, R. E.  Abadeh, M. S. 
Treść / Zawartość http://www.ibib.waw.pl/pl/wydawnictwa/biocybernetics-and-biomedical-enginering-bbe/bbe-tomy http://www.journals.elsevier.com/biocybernetics-and-biomedical-engineering/
Warianty tytułu
Języki publikacji EN
Abstrakty
EN Non-small cell lung cancer (NSCLC) is the most common type of lung cancer; and is one of the leading causes of death in the world. Surgery combined with chemotherapy is the recommended treatment for NSCLC. Since chemotherapy is an expensive treatment for either medical staff or patients suffering from pain, this study attempts to construct an intelligent predictive model to predict the adjuvant chemotherapy (ACT) effectiveness/ futileness in the patients, in order to help futile cases for unnecessary applications. There is a 2-step method: preprocessing and predicting. First a purposefully preprocessing tech-nique: chi-square test, SVM-RFE and correlation matrix, were employed in NSCLC gene expression dataset as a novel multi-layered feature selection method to defeat the curse of dimension and detect the chemotherapy target genes from tens of thousands features, based on which the patients can be classified into two groups, with NB classifier at second step. 10-Fold cross-validation was found with accuracy of 68.93% for 2 genes, TGFA (205015_s_at) and SEMA6C (208100_x_at), which is preferable compared to earlier studies, even though more than 2 input features are employed for the prediction. According to the results found in this study, one can concludes that the multi-layered feature selection approach has increased the classification accuracy in terms of finding the fitted patient for receiving ACT by reducing the number of features and has significant power to be used in medical datasets with small train samples and large number of features.
Słowa kluczowe
PL ekspresja genu   klasyfikator naiwny Bayesa   macierz korelacji   selekcja cech   chemioterapia  
EN gene expression   naïve Bayes   correlation matrix   feature selection   chemotherapy  
Wydawca Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences
Elsevier
Czasopismo Biocybernetics and Biomedical Engineering
Rocznik 2017
Tom Vol. 37, no. 3
Strony 477--488
Opis fizyczny Bibliogr. 52 poz., rys., tab., wykr.
Twórcy
autor Naftchali, R. E.
autor Abadeh, M. S.
  • Faculty of Electrical and Computer Engineering, Tarbiat Modares University, Tehran, Iran, saniee@modares.ac.ir
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Uwagi
PL Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017).
Kolekcja BazTech
Identyfikator YADDA bwmeta1.element.baztech-d317c4e6-167c-451f-ab33-8b860e1ef5e0
Identyfikatory
DOI 10.1016/j.bbe.2017.05.002