Discrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification

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Monica Triyani
Adiwijaya Adiwijaya
Annisa Aditsania


Cancer is one of the leading causes of death worldwide. According to the World Health Organization (WHO) in 2018, about 9.6 million deaths caused by cancer. DNA microarray technology has played an important role in analyzing and diagnosing cancer. However, microarray data has a large data dimensions resulting in the accuracy of the Random Forest are not optimal. In this paper, the Discrete Wavelet Transform (DWT) is selected as a feature extraction method. Based on the simulation, the dimension can be reduced and improve the accuracy of classification up to 8% - 20%. DWT approximation coefficient can improve accuracy better than detailed coefficients for data on colon cancer 100%, lung cancer 100%, ovarian 100%, prostate tumor 85.71%, and central nervous system 83.33%.


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How to Cite
M. Triyani, A. Adiwijaya, and A. Aditsania, “Discrete Wavelet Transform (DWT) and Random Forest for Cancer Detection Based on Microarray Data Classification”, INFOTEL, vol. 12, no. 3, Aug. 2020.


[1] American Cancer Society, "Surveillance Research," p. 5, 2019.
[2] World Health Organization, "Cancer Factsheets," World Health Organization, 2018. [Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cancer. [Accessed: 19-Sep-2019].
[3] Adiwijaya, U. N. Wisesty, E. Lisnawati, A. Aditsania, and D. S. Kusumo, "Dimensionality reduction using Principal Component Analysis for cancer detection based on microarray data classification," J. Comput. Sci., vol. 14, no. 11, pp. 1521–1530, 2018.
[4] W. Yip, S. B. Amin, and C. Li, Handbook of Statistical Bioinformatics. 2011.
[5] W. Astuti and A. Adiwijaya, "Principal Component Analysis Sebagai Ekstraksi Fitur Data Microarray Untuk Deteksi Kanker Berbasis Linear Discriminant Analysis," J. Media Inform. Budidarma, vol. 3, no. 2, pp. 72–77, 2019.
[6] R. Nurviarelda, A. A. Rohmawati, F. Informatika, U. Telkom, F. Informatika, and U. Telkom, "Klasifikasi Data Microarray Menggunakan Discrete Wavelet Transform Dan Naive Bayes Classification ", vol. 5, no. 1, pp. 1536–1540, 2018.
[7] Adiwijaya, "Deteksi Kanker Berdasarkan Klasifikasi Microarray Data," Media Inform. Budidarma, vol. 2, no. 4, pp. 181–186, 2018.
[8] K. Moorthy and M. S. Mohammad, "Random forest for gene selection and microarray data classification," no. July, 2013.
[9] 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.
[10] L. Breiman, "Random Forest Draft," pp. 1–33, 2001.
[11] D. H. Mazumder and R. Veilumuthu, "An Enhanced Gene Selection Methodology for Effective Microarray Cancer Data Classification," Int. J. Simul. Syst. Sci. Technol., pp. 1–7, 2018.
[12] Khadijah and H. S., "Klasifikasi Data Microarray Menggunakan Discrete Wavelet Transform dan Extreme Learning Machine," IJCCS (Indonesian J. Comput. Cybern. Syst., vol. 9, no. 1, pp. 33–42, 2015.
[13] Y. Liu, "Detect key gene information in classification of microarray data," EURASIP J. Adv. Signal Process., vol. 2008, 2008.
[14] J. Bennet, C. A. Ganaprakasam, and K. Arputharaj. "A Discrete Wavelet based Feature Extraction and Hybrid Classification Technique for Microarray Data Analysis". Anna University, Department of Computer Science and Engineering, 2014
[15] P. Liashchynskyi, "Grid Searh, Random Search, Genetic Algorithm : A Big Comparison for NAS", Cornell University, 2019.
[16] M.D. Purbolaksono, K. C. Widiastuti, Adiwijaya, 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, p. 012011. IOP Publishing, 2018.
[17] I. Damayana, R. D. Atmaja, and H. Fauzi, "Menggunakan Wevelet Transform Detection of Skin Cancer Melanoma Based on Digital Image," Deteksi Kanker Kulit Melanoma Berbas. Pengolah. Citra Menggunakan Wevelet Transform, vol. 3, no. 3, pp. 4718–4723, 2016.

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