Deteksi Kanker Berdasarkan Klasifikasi Data Microarray Menggunakan Least Absolute Shrinkage and Selection Operator dan Functional Link Neural Network
DOI:
https://doi.org/10.30865/mib.v4i4.2349Keywords:
Cancer Detection, DNA Microarray, Dimension Reduction, Least Absolute Shrinkage and Selection Operator (LASSO), Functional Link Neural Network (FLNN)Abstract
Cancer is a dangerous disease that arises from the conversion of normal cells into tumor cells that develop into malignant tumors. According to WHO, cancer is the second deadliest disease in the world. About 70% of cancer deaths occur in low and middle income countries such as Indonesia. Cancer can be detected by recognizing patterns of expression of human genes. DNA Microarray is a technology that can find patterns of gene expression in a variety of different conditions by means of microarray data classification. Microarray data has very large dimensions and needs to be reduced in order to obtain informative genes to detect cancer optimally. In this study, the authors use the Least Absolute Shrinkage and Selection Operator (LASSO) as a feature selection method to reduce data dimensions and Functional Link Neural Network (FLNN) as a classification method with Legendre Polynomial base functions. With a series of processes that have been carried out, obtained an average accuracy of 86.41% and an average f1-score of 81.83%
References
World Health Organization, “Cancer,†[Online]. Available: https://www.who.int/news-room/fact-sheets/detail/cancer. [Accessed: 20 Januari 2020].
Kementrian Kesehatan Republik Indonesia. “Hari Kanker Sedunia 2019â€. [Online]. Available: https://www.depkes.go.id/article/view/19020100003/hari-kanker-sedunia-2019.html. [Accessed: 24 Januari 2020].
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. Journal of Computer Science 14(11), 2018.
A. Bharathi & A.M. Natarajan, “Cancer Classification of Bioinformatics data using ANOVA,†in International journal of computer theory and engineering, 2, 369, 2010.
A. Adiwijaya, “Deteksi Kanker Berdasarkan Klasifikasi Microarray Data,†J. MEDIA Inform. BUDIDARMA, 2018, doi: 10.30865/mib.v2i4.1043.
H. Aydadenta and Adiwijaya, “A clustering approach for feature selection in microarray data classification using random forest,†J. Inf. Process. Syst., 2018, doi: 10.3745/JIPS.04.0087.
M. S. Pratiwi, Adiwijaya, and A. Aditsania, “Cancer Detection Based on Microarray Data Classification using Genetic Bee Colony (GBC) and Conjugate Gradient Backpropagation with Modified Polak Ribiere (MBP-CGP),†in 2018 International Conference on Computer, Control, Informatics and its Applications: Recent Challenges in Machine Learning for Computing Applications, IC3INA 2018 - Proceeding, 2019, doi: 10.1109/IC3INA.2018.8629538
W. Zhongxin, S. Gang, Z. Jing, Zhao, J, “Feature Selection Algorithm Based on Mutual Information and Lasso for Microarray Data,†in The Open Biotechnology Journal, 10, 278-286, 2016.
V. Fonti, “Feature Selection using LASSO,†in Research Paper in Business Analytics. 2017.
E. M. Mashhour, E.M.F.E. Houby, K. Wassif, A. I. Salah, “Survey on different Methods for Classifying Gene Expression using Microarray Approach,†in International Journal of Computer Applications, 2016.
I. Mabarti, “Implementation of Minimum Redundancy Maximum Relevance (MRMR) and Genetic Algorithm (GA) for Microarray Data Classification with C4. 5 Decision Tree,†in Journal of Data Science and Its Applications, 3(1), 38-47. 2020.
M. D. Purbolaksono, K. C. Widiastuti, M. S. Mubarok, Adiwijaya, and F. A. Ma’ruf, “Implementation of mutual information and bayes theorem for classification microarray data,†in Journal of Physics: Conference Series, 2018, doi: 10.1088/1742-6596/971/1/012011.
P. T. RAMADHANI, “Deteksi Kanker berdasarkan Klasifikasi Data Microarray menggunakan Functional Link Neural Network dengan Seleksi Fitur Genetic Algorithm,†Indones. J. Comput., 2017, doi: 10.21108/indojc.2017.2.2.173.
I. Priyono, A. Adiwijaya, A. Aditsania, “Cancer Detection based on Microarray Data Classification Using Principal Component Analysis and Functional Link Neural Network,†in Journal of Data Science and Its Applications, 3(2). 2020.
M. Kumar, S. Singh, and S. K. Rath, “Classification of Microarray Data using Functional Link Neural Network,†in Procedia Computer Science, 2015, doi: 10.1016/j.procs.2015.07.463.
Jinyan, Li, Kent-ridge bio-medical data set repository. 2013.
Park, T., Yi, S., Kang, S. et al. “Evaluation of normalization methods for microarray data,†in BMC Bioinformatics 4, 33, 2003, doi: https://doi.org/10.1186/1471-2105-4-33.
S. Datta, J. Le-Rademacher, S. Datta, “Predicting Patient Survival from Microarray Data by Accelerated Failure Time Modeling Using Partial Least Squares and LASSO,†In Biometrics 63, 259-271. 2007. DOI: 10.1111/j.1541-0420.2006.00660.x
Y. M. Mohmad Hassim and R. Ghazali, “Functional link neural network - Artificial bee colony for time series temperature prediction,†in Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), 2013, doi: 10.1007/9783-642-39637-3-34.
D. M. Sahoo and S. Chakraverty, “Functional link neural network approach to solve structural system identification problems,†in Neural Comput. Appl., 2018, doi: 10.1007/s00521-017-2907-x.
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