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

Adiwijaya, Adiwijaya; Wisesty, Untari Novia; Lisnawati, Euis; Aditsania, Annisa; Kusumo, Dana Sulistyo

doi:10.3844/jcssp.2018.1521.1530

Cited by 69 publications

(53 citation statements)

References 10 publications

(8 reference statements)

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“…Penelitian selanjutnya dilakukan pada tahun 2018 [6]. Principal Component Analysis (PCA) digunakan dalam proses reduksi dimensi [21,22] dan penggunaan Suport Vector Machine (SVM) dan Levenberg Marquardt based Back Propagation (LMBP) sebagai metode klasifikasi. Hasil akurasi yang didapat dengan LMBP sebesar 96,07% sedangkan dengan SVM sebesar 94,98%.…”

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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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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“…Microarray gene expressions can differ by an order of magnitude. us, it is necessary to normalize these data to improve the performance of subsequent microarray data analysis stages like gene selection/feature extraction, clustering, and classification [1].…”

Section: Pso-pca-lgp-mcsvm Principlesmentioning

confidence: 99%

“…In this paper, the microarray gene expressions are linearly transformed from the interval [X min , X max ] ⟶ [0, 1] uniformly utilizing the following equation [1]:…”

Section: Pso-pca-lgp-mcsvm Principlesmentioning

confidence: 99%

“…Cancer is a disorder caused by excessive and uncontrolled cell division in a body. A total of 9.6 million people died of cancer in 2018 [1]. As a matter of fact, death due to cancer can be reduced to nearly half if the cancer types are detected early and the right treatment administered in time.…”

Section: Introductionmentioning

confidence: 99%

“…To improve the classification accuracy of the SVM classifier, some techniques have been presented to search for the optimal model parameters, such as the grid-search and the gradient descent [1]. Although these approaches have proven their effectiveness in the corresponding experiments, in most cases they fall into the local optimum point easily and have a defect of low efficiency [1,18].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

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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Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

Nirmalakumari¹,

Rajaguru²,

Rajkumar

2020

Int J Imaging Syst Tech

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Cancer disease is accountable for many deaths that are over 9.6 million in 2018 and roughly one out of six deaths occur because of cancer worldwide. The colon cancer is the second prominent source of death of around 1.8 million cases. This research is inclined to detect the colon cancer from microarray dataset. It will aids the experts to distinguish the cancer cells from normal cells for appropriate determination and treatment of cancer at earlier stages that leads to increase the survival rate of the patients. The high dimensionality in microarray dataset with less samples and more attributes creates lag in the detection capability of the classifier. Hence there is a need for dimensionality reduction techniques to preserve the significant genes that are prominent in the disease classification. In this article, at first ANOVA method used to select the best genes and then principal component analysis (PCA) and fuzzy C‐means clustering (FCM) techniques are further employed to choose relevant genes. The PCA and FCM features are classified using model, discriminant, regression, hybrid, and heuristic‐based classifiers. The attained results show that the heuristic classifier with PCA features is encapsulated an average classification accuracy of 97.92% for classifying both the colon cancer and normal samples. Also, for FCM features, the Heuristic classifier is maintained at an average classification accuracy of 99.48% and 97.92% for classifying the colon cancer and normal samples, respectively. The Heuristic classifier outperforms with high accuracy than all other classifiers in the classification of colon cancer.

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

Cited by 69 publications

References 10 publications

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

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

Performance analysis of classifiers for colon cancer detection from dimensionality reduced microarray gene data

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