Momentum Backpropagation Optimization for Cancer Detection Based on DNA Microarray Data

Untari Novia Wisesty(1*), Febryanti Sthevanie(2), Rita Rismala(3),

(1) School of Computing, Telkom University
(2) School of Computing, Telkom University
(3) School of Computing, Telkom University
(*) Corresponding Author


Early detection of cancer can increase the success of treatment in patients with cancer. In the latest research, cancer can be detected through DNA Microarrays. Someone who suffers from cancer will experience changes in the value of certain gene expression.  In previous studies, the Genetic Algorithm as a feature selection method and the Momentum Backpropagation algorithm as a classification method provide a fairly high classification performance, but the Momentum Backpropagation algorithm still has a low convergence rate because the learning rate used is still static. The low convergence rate makes the training process need more time to converge. Therefore, in this research an optimization of the Momentum Backpropagation algorithm is done by adding an adaptive learning rate scheme. The proposed scheme is proven to reduce the number of epochs needed in the training process from 390 epochs to 76 epochs compared to the Momentum Backpropagation algorithm. The proposed scheme can gain high accuracy of 90.51% for Colon Tumor data, and 100% for Leukemia, Lung Cancer, and Ovarian Cancer data.


Momentum Backpropagation with Adaptive Learning Rate Neural Network Genetic Algorithm DNA Microarray Early Cancer Detection

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“Cancer,” 2018. [Online]. Available: [Accessed: 31-May-2020].

World Health Organization, “Promoting Cancer Early Diagnosis,” 2017. [Online]. Available: [Accessed: 14-Aug-2020].

X. Chen et al., “Non-invasive early detection of cancer four years before conventional diagnosis using a blood test,” Nat. Commun., vol. 11, no. 1, p. 3475, Dec. 2020.

Y. Lu and J. Han, “Cancer classification using gene expression data,” Inf. Syst., vol. 28, no. 4, pp. 243–268, Jun. 2003.

J. Wu, “Feature Selection for Cancer Classification Using Microarray Gene Expression Data,” Biostat. Biometrics Open Access J., vol. 1, no. 2, Apr. 2017.

R. Bumgarner, “Overview of DNA Microarrays: Types, Applications, and Their Future,” in Current Protocols in Molecular Biology, Hoboken, NJ, USA: John Wiley & Sons, Inc., 2013.

J. Garaizar, A. Rementeria, and S. Porwollik, “DNA microarray technology: a new tool for the epidemiological typing of bacterial pathogens?,” FEMS Immunol. Med. Microbiol., vol. 47, no. 2, pp. 178–189, Jul. 2006.

G. Russo, C. Zegar, and A. Giordano, “Advantages and limitations of microarray technology in human cancer,” Oncogene, vol. 22, no. 42, pp. 6497–6507, Sep. 2003.

S. Kaushik, S. Kaushik, and D. Sharma, “Functional Genomics,” in Encyclopedia of Bioinformatics and Computational Biology, Elsevier, 2019, pp. 118–133.

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

A. Nurfalah, Adiwijaya, and A. A. Suryani, “CANCER DETECTION BASED ON MICROARRAY DATA CLASSIFICATION USING PCA AND MODIFIED BACK PROPAGATION,” Far East J. Electron. Commun., vol. 16, no. 2, pp. 269–281, May 2016.

Seeja.K.R. and Shweta, “Microarray Data Classification Using Support Vector Machine,” Int. J. Biometrics Bioinforma., vol. 5, no. 1, 2011.

M. Kumar, S. Singh, and S. K. Rath, “Classification of Microarray Data using Functional Link Neural Network,” Procedia Comput. Sci., vol. 57, pp. 727–737, 2015.

A. Bharathi and A. M. Natarajan, “Cancer Classification of Bioinformatics datausing ANOVA,” Int. J. Comput. Theory Eng., pp. 369–373, 2010.

R. Díaz-Uriarte and S. Alvarez de Andrés, “Gene selection and classification of microarray data using random forest,” BMC Bioinformatics, vol. 7, no. 3, 2006.

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, Nov. 2018.

U. N. Wisesty, B. P. B. Pratama, A. Aditsania, and Adiwijaya, “Cancer Detection Based on Microarray Data Classification Using Deep Belief Network and Mutual Information,” in 2017 5th International Conference on Instrumentation, Communications, Information Technology, and Biomedical Engineering (ICICI-BME), 2017, pp. 157–162.

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,” J. Phys. Conf. Ser., vol. 971, p. 012018, Mar. 2018.

“ELVIRA Biomedical Data Set Repository,” 2005. [Online]. Available:

U. N. Wisesty, “Levenberg-Marquardt Neural Network for Eye States Detection Based on Electroencephalography Data,” Int. J. Inf. Commun. Technol., vol. 2, no. 1, p. 23, Jul. 2016.

S. Nayak, B. B. Choudhury, and S. K. Lenka, “Gradient Descent with Momentum Based Backpropagation Neural Network for Selection of Industrial Robot,” 2016, pp. 487–496.

H. Shao and G. Zheng, “A New BP Algorithm with Adaptive Momentum for FNNs Training,” in 2009 WRI Global Congress on Intelligent Systems, 2009, pp. 16–20.

K.-L. Du and M. N. S. Swamy, “Multilayer Perceptrons: Architecture and Error Backpropagation,” in Neural Networks and Statistical Learning, London: Springer London, 2019, pp. 97–141.

Y. Wu, Z. Bao, Y. Yuan, L. Zhang, and Y. Feng, “Application of momentum backpropagation algorithm (MOBP) in identification of low-resistivity pay zones in sandstones,” J. Pet. Explor. Prod. Technol., vol. 7, no. 1, pp. 23–32, Mar. 2017.

Chien-Cheng Yu and Bin-Da Liu, “A backpropagation algorithm with adaptive learning rate and momentum coefficient,” in Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN’02 (Cat. No.02CH37290), pp. 1218–1223.

N. O. Attoh-Okine, “Analysis of learning rate and momentum term in backpropagation neural network algorithm trained to predict pavement performance,” Adv. Eng. Softw., vol. 30, no. 4, pp. 291–302, Apr. 1999.

R. Abd Rahman, R. Ramli, Z. Jamari, and K. R. Ku-Mahamud, “Evolutionary Algorithm with Roulette-Tournament Selection for Solving Aquaculture Diet Formulation,” Math. Probl. Eng., vol. 2016, pp. 1–10, 2016.

Z. Pliszka and O. Unold, “On the Ability of the One-Point Crossover Operator to Search the Space in Genetic Algorithms,” 2015, pp. 361–369.

R. Kala, “Optimization-Based Planning,” in On-Road Intelligent Vehicles, Elsevier, 2016, pp. 109–150.

F. Vavak and T. C. Fogarty, “Comparison of steady state and generational genetic algorithms for use in nonstationary environments,” in Proceedings of IEEE International Conference on Evolutionary Computation, pp. 192–195.



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