Cancer Detection Based on Microarray Data Classification Using Pca and Modified Back Propagation

Nurfalah, Adiyasa; Adiwijaya, Adiwijaya; Suryani, Arie Ardiyanti

doi:10.17654/ec016020269

Cited by 21 publications

(17 citation statements)

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“…Using leukemia data, the scheme achieved a faster running time than ANN. Nurfalah et al (2016) developed a scheme using PCA and MBP (Modified Backpropagation using Conjugate Gradient) as the dimension reduction method and microarray data classification, respectively. The scheme yielded 96% accuracy for ovarian cancer, 76.92% for colon cancer and 97.14% for leukemia data.…”

Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification

Adiwijaya¹,

Wisesty²,

Lisnawati³

et al. 2018

Journal of Computer Science

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Cancer is one of the most deadly diseases in the world. The International Agency for Research on Cancer (IARC) noted 14.1 million new cancer cases and 8.2 million deaths from cancer in 2012. In the last few years, DNA microarray technology has increasingly been used to analyze and diagnose cancer. Analysis of gene expression data in the form of microarray allows medical experts to ascertain whether or not a person suffers from cancer. DNA microarray data has a large dimension that can affect the process and accuracy of cancer classification. Therefore, a classification scheme that includes dimension reduction is needed. In this research, a Principal Component Analysis (PCA) dimension reduction method that includes the calculation of variance proportion for eigenvector selection was used. For the classification method, a Support Vector Machine (SVM) and Levenberg-Marquardt Backpropagation (LMBP) algorithm were selected. Based on the tests performed, the classification method using LMBP was more stable than SVM. The LMBP method achieved an average 96.07% accuracy, while the SVM achieved 94.98% accuracy.

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Section: Introductionmentioning

confidence: 99%

Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification

Adiwijaya¹,

Wisesty²,

Lisnawati³

et al. 2018

Journal of Computer Science

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“…Penelitian ini menggunakan dataset colon yang mana hasil terbesar akurasi yang didapat sebesar 97% dengan menggunakan metode klasifikasi SVM dengan kernel linear. Penelitian terakhir dilakukan tahun 2015 yang berjudul "Cancer Detection Based On Microarray Data Classification Using PCA and MBP" [9]. Penelitian ini bertujuan untuk mendeteksi kanker berdasarkan data microarray.…”

Section: Pendahuluanunclassified

Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray

Naufal

2020

Jur. Ris. Kom.

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Cancer is a disease that can cause human death in various countries. According to WHO in 2018, cancer causes 9.6 million human deaths worldwide. Globally, about 1 in 6 deaths is due to cancer. Therefore, we need a technology that can be used for cancer detection with high acuration so that cancer can be detected early. Microarrays technique can predict certain tissues in humans and can be classified as cancer or not. However, microarray data has a problem with very large dimensions. To overcome this problem, in this study use one of the dimension reduction techniques, namely Partial Least Square(PLS) and use Support vector Machine (SVM) and K-Nearest Neighbors as a classification method, which will be used to compare which is better.The system built was able to reach 98.54% in leukemia data with PLS-KNN, 100% in lung data with KNN, 66.52% in breast data with PLS-KNN, and 85.60% in colon data with PLS- SVM. KNN is able to get the best in three data from four valued data.

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“…us, a process that can reduce the dimensionality complexity of this type of data is required. In addition, a dimensionality reduction step will minimize errors obtained in the subsequent classification stage [1,12,[33][34][35].…”

Section: Principal Component Analysis (Pca)mentioning

confidence: 99%

“…ough next-generation sequencing (NGS) especially RNA-sequencing (RNA-Seq) is slowly replacing microarrays when analyzing and identifying complex mechanism in gene expression, e.g., in the gene expression-based cancer classification problem, it is relatively expensive compared to microarrays. Since microarrays have been used for a long time, there exist robust statistical and operational methods for their processing [4][5][6][7][8][9][10][11][12][13]. In addition, many significant microarray experiments have been conducted and are publicly available to the research community [14][15][16][17][18][19][20].…”

Section: Introductionmentioning

confidence: 99%

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Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

Segera

Mbuthia

Nyete

2019

BioMed Research International

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Determining an optimal decision model is an important but difficult combinatorial task in imbalanced microarray-based cancer classification. Though the multiclass support vector machine (MCSVM) has already made an important contribution in this field, its performance solely depends on three aspects: the penalty factor C, the type of kernel, and its parameters. To improve the performance of this classifier in microarray-based cancer analysis, this paper proposes PSO-PCA-LGP-MCSVM model that is based on particle swarm optimization (PSO), principal component analysis (PCA), and multiclass support vector machine (MCSVM). The MCSVM is based on a hybrid kernel, i.e., linear-Gaussian-polynomial (LGP) that combines the advantages of three standard kernels (linear, Gaussian, and polynomial) in a novel manner, where the linear kernel is linearly combined with the Gaussian kernel embedding the polynomial kernel. Further, this paper proves and makes sure that the LGP kernel confirms the features of a valid kernel. In order to reveal the effectiveness of our model, several experiments were conducted and the obtained results compared between our model and other three single kernel-based models, namely, PSO-PCA-L-MCSVM (utilizing a linear kernel), PSO-PCA-G-MCSVM (utilizing a Gaussian kernel), and PSO-PCA-P-MCSVM (utilizing a polynomial kernel). In comparison, two dual and two multiclass imbalanced standard microarray datasets were used. Experimental results in terms of three extended assessment metrics (F-score, G-mean, and Accuracy) reveal the superior global feature extraction, prediction, and learning abilities of this model against three single kernel-based models.

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Cancer Detection Based on Microarray Data Classification Using Pca and Modified Back Propagation

Cited by 21 publications

References 0 publications

Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification

Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification

Analisis Perbandingan Klasifikasi Support Vector Machine (SVM) dan K-Nearest Neighbors (KNN) untuk Deteksi Kanker dengan Data Microarray

Particle Swarm Optimized Hybrid Kernel-Based Multiclass Support Vector Machine for Microarray Cancer Data Analysis

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