Classification of breast cancer microarray data using Radial Basis Function Network

Mazlan, Umi Hanim; Saad, Puteh

doi:10.1109/icssbe.2012.6396523

Cited by 5 publications

(1 citation statement)

References 88 publications

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“…Multiple studies reported using IG in their study [20,23,25]. IG can be identified as a classical popular feature selection in microarray data as it is reliable and less time consuming.…”

Section: Feature Selectionmentioning

confidence: 99%

Classification of Breast Cancer Subtypes using Microarray RNA Expression Data

Muhammad Shazwan Suhiman,

Sayang Mohd Deni,

Ahmad Zia Ul-Saufie Mohamad Japeri

et al. 2024

ARASET

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Breast cancer is a heterogeneous disease that involves molecular alteration, cellular alterations, and clinical outcome for which the classification of Breast cancer remains a challenge to diagnose. Current practice uses immunohistochemistry markers and clinical variables to classify Breast cancer, but this approach has limitations due to the inclusion of other tumour subtypes and healthy individuals. Machine learning approaches based on mRNA expression data offer new possibilities for researchers to investigate the potential of molecular biomarkers as one of the diagnostic characteristics. The purpose of this study is to evaluate features (genes) rank through feature selection method for Breast cancer diagnostic test. Three feature selection methods of IG, relief and mRMR were applied and subsets of top 100, 50, 25, 10, 5 and 3 were created. Each subset was tested with SVM, LR and RF classifiers and its performance was assessed using confusion matrix. The result of this study found that the feature selection of IG, reliefF and mRMR was able to achieve highest accuracy with SVM, LR and RF classifier. mRMR with RF classifier achieved highest accuracy with the least number of top rank genes with 25 genes. Hybrid feature selection approached (mRMR + SVM) improved accuracy of top 3 highest rank genes using SVM, LR and RF classifier. Future work should aim to use other feature selection methods and classifiers to explore the classification accuracy with the least features subset in multiclass cancer dataset.

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“…Multiple studies reported using IG in their study [20,23,25]. IG can be identified as a classical popular feature selection in microarray data as it is reliable and less time consuming.…”

Section: Feature Selectionmentioning

confidence: 99%

Classification of Breast Cancer Subtypes using Microarray RNA Expression Data

Muhammad Shazwan Suhiman,

Sayang Mohd Deni,

Ahmad Zia Ul-Saufie Mohamad Japeri

et al. 2024

ARASET

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The classification of breast cancer with Machine Learning Techniques

Kolay

Erdoğmuş

2016

2016 Electric Electronics, Computer Science, Biomedical Engineerings' Meeting (EBBT)

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ÖzetçeBu çalışmada UCI(University of California-Irvine) Machine Learning Laboratuvarından alınan göğüs kanseri dataları kullanılarak, makine öğrenmesi teknikleri ile göğüs kanserinin sınıflandırılmasına çalışılmıştır. Bu amaçla, göğüs kanseri dataları kullanılarak, K-means kümeleme uzaklık ölçümlerinin kümeleme başarısı Matlab©'da analiz edilmiştir. Daha sonra, hiçbir ön işleme yapmadan, Weka© Veri Madenciliği yazılımı kullanılarak, UCI'den alınan göğüs kanseri dataları, çeşitli makine öğrenme teknikleri ile WEKA'da sınıflandırılmıştır. Sonuç olarak kümeleme uzaklık ölçütlerinin başarıyı yaklaşık olarak %12 değiştirdiği ve sınıflandırma başarısının yöntemlere göre %45-%79 arasında değiştiği görülmüştür. AbstractIn this study, it is aimed to classify breast cancer data attained from UCI(University of California-Irvine), Machine Learning Laboratory with some Machine Learning Techniques. With this aim, clustering performance of some distance measures in Matlab© has been compared, using breast cancer data. Later without using any pre-processing, some of the machine learning techniques are used for the clustering breast cancer data, using WEKA data mining software©. As a result, it has been seen that distance measures effects the clustering performance nearly 12 percentage and the succes of the classification varies from %45 to %79, according to the methods.

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