Cancer Classification of Bioinformatics datausing ANOVA

Bharathi, A.; Natarajan, A. M.

doi:10.7763/ijcte.2010.v2.169

Cited by 29 publications

(17 citation statements)

References 18 publications

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“…Sel kanker tersebut dapat menyusup ke jaringan sekitar tumor sehingga tumor tersebut sudah diklasifikasikan kedalam kanker [5]. Dibandingkan dengan mendeteksi kanker secara tradisional yaitu berdasarkan analisis kemunculan tumor, deteksi kanker melalui ekspresi gen jauh lebih terpercaya dan akurat [3]. Mendeteksi kanker melalui ekspresi gen akan sangat membantu para ahli medis dalam penanganan bagi pasien yang menderita kanker sehingga dapat menekan angka kematian yang kian bertambah.…”

Section: Open Accessunclassified

“…Juga dengan memainkan kernel pada SVM, dapat diketahui kernel mana yang menghasilkan akurasi paling besar untuk klasifikasi. Berdasarkan kajian yang telah dilakukan oleh Bharathi dan Natarajam, akurasi terbaik yang dihasilkan SVM dan ANOVA yaitu sebesar 97,91%, lebih besar bila dibandingkan dengan T-Test dan BPN [1], dan berdasarkan kajian yang dilakukan oleh Devi Arockia Vanitha dan tim, akurasi terbesar dihasilkan oleh SVM dengan kernel linear yaitu sebesar 97,7% [3] II. STUDI LITERATUR Berbagai penelitian telah dilakukan oleh para ahli untuk menangani masalah dimensi tinggi yang dimiliki oleh data microarray juga metode yang tepat untuk pengklasifikasian data tersebut.…”

Section: Open Accessunclassified

“…All menggunakan metode seleksi fitur T-test dan Functional Link Neural Network (FLNN) sebagai classifier dengan tingkat akurasi yang dihasilkan sebesar 97,22% [1]. Bharathi and Natarajan menggunakan seleksi fitur Analysis of Variance (ANOVA) dan Support Vector Machine (SVM) sebagai classifier dengan tingkat akurasi 97,91% [3]. Selanjutnya Diaz et.…”

Section: Open Accessunclassified

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Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker

Diani

2017

IndoJC

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Based on data from the Center for Data and Information Ministry of Health, in 2012 about 8.2 million cases of death caused by cancer. Recent developments show that DNA microarray technology is able to handle cancer detection problems early on, but the main disadvantage of microarray is the problem of curse of dimensionality. Analysis of Variance (ANOVA) is one of the feature selection methods that can overcome the weakness of microarray. ANOVA can find an informative gene pair that can assist in the classification process performed by the Support Vector Machine (SVM). In SVM, the kernel trick when learning model is helpful in overcoming the feature space problem. The selection of the kernel affects the resulting accuracy. Through a series of processes such as correlation calculations, feature selection and classification using SVM, accuracy is obtained from the four datasets used. For leukemia and ovarian cancer datasets, the greatest accuracy is generated by the polynomial kernel at 100% and 97.54% with the parameter values of 5 .. As for the largest lung cancer accuracy dataset obtained from linear kernel that is equal to 100% with the parameter value 0 . 1  C and for the dataset colon tumor the greatest accuracy is obtained from the RBF kernel of 85.15% with the parameter value 5 . 1  C 5 . 0   . The kernel difference that produces the highest accuracy on each dataset is highly dependent on the characteristics of the cancer dataset itself. Keywords: cancer detection, DNA microarray, dimension reduction, correlation, analysis of variance, support vector machine, kernel trick AbstrakBerdasarkan data dari Pusat Data dan Informasi Kementrian Kesehatan RI, di tahun 2012 sekitar 8,2 juta kasus kematian disebabkan oleh kanker. Perkembangan terakhir menunjukan bahwa teknologi DNA microarray mampu menangani masalah deteksi kanker sejak dini, namun kelemahan utama dari microarray adalah masalah curse of dimensionality. Analysis of Variance (ANOVA) merupakan salah satu metode seleksi fitur yang dapat mengatasi kelemahan microarray. ANOVA dapat menemukan pasangan gen informatif yang dapat membantu dalam proses pengklasifikasian yang dilakukan oleh Support Vector Machine (SVM). Dalam SVM, kernel trick saat learning model sangat membantu dalam mengatasi masalah feature space. Pemilihan kernel berpengaruh terhadap akurasi yang dihasilkan. Melalui serangkaian proses seperti perhitungan korelasi, seleksi fitur dan pengklasifikasian menggunakan SVM, didapatkan akurasi dari empat dataset yang digunakan. Untuk dataset leukimia dan ovarian cancer, akurasi terbesar dihasilkan oleh kernel polynomial yaitu sebesar 100% dan 97,54% dengan nilai parameter 5 .

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Section: Open Accessunclassified

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Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker

Diani

2017

IndoJC

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“…For a given test point x t , the k th rule of FIS model is represented in the following form: IF x 1 is A 1 and x 2 is A 2 ,… THEN (13) where x 1 , x 2,… xn are input variables, and is a fuzzy set, R k is a linear function. The fuzzy set uses a Gaussian function and can be computed using Equation 14. The parameters b 0 and p t of rules are computed using least mean squares (LMS) algorithm [12].…”

Section: Subtractive Clustering (Sc)mentioning

confidence: 99%

Classification of microarray data using Fuzzy inference system

Kumar

Rath

2014

2014 International Conference on Recent Trends in Information Technology

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The DNA microarray classification is one of the most popular technique among researchers and practitioners. In microarray data analysis, huge useful information may be lost due to irrelevant and insignificant features of the dataset. To overcome this drawback of the data set, only those features are selected which have high relevance with the classes and high significance in the feature set. In this paper, the t-statistic is used for feature selection with high relevance; and Fuzzy inference system (FIS) has been presented for classification purpose. FIS model is applied to classify the leukemia data set for gene classification. A comparative analysis of Fuzzy inference system (FIS) with different set of features (genes) have been presented. The comparison was performed on the basis of available performance parameters in literature such as: precision, recall, specificity, F-Measure, ROC curve and accuracy. The obtained results have been critically examined with the existing classifiers in the literature and it is observed that the proposed system obtained promising results with an increase in classification accuracy rate.

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“…These methods present cutting edge research topics and methodologies for the purpose of facilitating collaboration between researchers and bioinformaticians. Mining bioinformatics data is a rising field at the intersection of bioinformatics and data mining [14]. Some of them belong to the category of data mining that decides whether or not an example not yet noticed is of a predefined type.…”

Section: Introductionmentioning

confidence: 99%

An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN

Kumar¹

2012

IJACSA

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Cancer Classification of Bioinformatics datausing ANOVA

Cited by 29 publications

References 18 publications

Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker

Analisis Pengaruh Kernel Support Vector Machine (SVM) pada Klasifikasi Data Microarray untuk Deteksi Kanker

Classification of microarray data using Fuzzy inference system

An Effective Identification of Species from DNA Sequence: A Classification Technique by Integrating DM and ANN

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