Principal Component Analysis Sebagai Ekstraksi Fitur Data Microarray Untuk Deteksi Kanker Berbasis Linear Discriminant Analysis

 (*)Widi Astuti Mail (Fakultas Informatika, Universitas Telkom, Indonesia)
 Adiwijaya Adiwijaya (Fakultas Informatika, Universitas Telkom, —)

(*) Corresponding Author

DOI: http://dx.doi.org/10.30865/mib.v3i2.1161

Abstract

Cancer is one of the leading causes of death globally. Early detection of cancer allows better treatment for patients. One method to detect cancer is using microarray data classification. However, microarray data has high dimensions which complicates the classification process. Linear Discriminant Analysis is a classification technique which is easy to implement and has good accuracy. However, Linear Discriminant Analysis has difficulty in handling high dimensional data. Therefore, Principal Component Analysis, a feature extraction technique is used to optimize Linear Discriminant Analysis performance. Based on the results of the study, it was found that usage of Principal Component Analysis increases the accuracy of up to 29.04% and f-1 score by 64.28% for colon cancer data.

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References

D. Kementerian Kesehatan Republik Indonesia, “Situasi Penyakit Kanker,” Jakarta Selatan, 2015.

Adiwijaya, “Deteksi Kanker Berdasarkan Klasifikasi Microarray Data,” Media Inform. Budidarma, vol. 2, no. 4, pp. 181–186, 2018.

R. Gonzalo and A. Sánche, “Introduction to Microarrays Technology and Data Analysis,” in Comprehensive Analytical Chemistry, 1st ed., vol. 82, J. Jaumot, C. Bedia, and R. Tauler, Eds. Amsterdam: Elsevier BV, 2018, pp. 37–69.

H. Aydadenta and Adiwijaya, “A Clustering Approach for Feature Selection in Microarray Data Classification Using Random Forest,” J. Inf. Process. Syst., vol. 14, no. 5, pp. 1167–1175, 2018.

M. Ghosh, S. Begum, R. Sarkar, D. Chakraborty, and U. Maulik, “Recursive Memetic Algorithm for gene selection in microarray data,” Expert Syst. Appl., vol. 116, pp. 172–185, 2019.

C. Devi Arockia Vanitha, D. Devaraj, and M. Venkatesulu, “Gene Expression Data Classification Using Support Vector Machine and Mutual Information-based Gene Selection,” in Procedia Computer Science, 2015, vol. 47, pp. 13–21.

Nurfalah, A. Adiwijaya, and Suryani, A.A., (2016). Cancer Detection Based On Microarray Data Classification Using PCA And Modified Back Propagation. Far East Journal of Electronics and Communications, 16(2), p.269.

U. N. Wisesty, R. S. Warastri, and S. Y. Puspitasari, “Leukemia and colon tumor detection based on microarray data classification using momentum backpropagation and genetic algorithm as a feature selection method,” in Journal of Physics: Conference Series (Vol. 971, No. 1), 2018, pp. 12–18.

M. D. Purbolaksono, K. C. Widiastuti, M. S. Mubarok, and F. A. Ma’ruf, “Implementation of mutual information and bayes theorem for classification microarray data,” in Journal of Physics: Conference Series (Vol. 971, No. 1), 2018.

W. K. Yip, S. B. Amin, and C. Li, “A survey of classification techniques for microarray data analysis,” in Handbook of Statistical Bioinformatics, Springer, 2011, pp. 193–223.

Adiwijaya, U. N. Wisesty, E. Lisnawati, A. Aditsania, D. S. Kusumo, "Dimensionality Reduction using Principal Component Analysis for Cancer Detection based on Microarray Data Classification", Journal of Computer Science 14(11), 2018, pp.1521-1530.

M. I. Khalid, T. Alotaiby, S. A. Aldosari, S. A. Alshebeili, F. S. Y. Al-Hameed, M.H. Almohammed, and T. . Alotaibi, “Epileptic MEG spikes detection using common spatial patterns and linear discriminant analysis,” IEEE Access, vol. 4, 2016.

E. Neto, F. Biessmann, H. Aurlien, H. Nordby, and T. Eichele, “Regularized linear discriminant analysis of EEG features in dementia patients,” Front. Aging Neurosci., vol. 8, p. 273, 2016.

D. H. Mazumder and R. Veilumuthu, “An Enhanced Gene Selection Methodology for Effective Microarray Cancer Data Classification,” Int. J. Simulation--Systems, Sci. Technol., vol. 19, no. 2, 2018.

Z. Jaadi, “A step by step explanation of Principal Component Analysis,” Towards Data Science, 2019. [Online]. Available: https://towardsdatascience.com/a-step-by-step-explanation-of-principal-component-analysis-b836fb9c97e2.

“Colon Tumor.” [Online]. Available: http://leo.ugr.es/elvira/DBCRepository/ColonTumor/ColonTumor.html.

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