Published 30 sep 2019 •  vol 130  • 



Bassam Al-Shargabi, Computer Information Dept, Middle East University, Amman, Jordan
Fida'a Al-Shami, Computer Science Dept, Middle East University, Amman, Jordan
Rami S. Alkhawaldeh, Computer Information Systems Dept, The University of Jordan, Aqaba, Jordan



Breast cancer (BC) is a standout disease of the most well-known cancers among women around the world. The analysis and prediction of BC leads to early manage the disease and protect the patients from further medical complications. In the light of its noticeable focal points in basic highlights identification from complex BC datasets, Machine Learning (ML) is generally perceived as the technique of decision in BC design order and gauge displaying. Because of the high performance of the Multi-layer Perceptron (MLP) algorithm as one of the ML techniques, we conducted experiments in order to enhance the accuracy rate of MLP by tuning its hyper-parameters along with studying the effect of feature selection methods and feature reduction of MLP. As feature selection results indicated that an increase in the number of input parameters tends to reduce the error associated with the estimator model. The tuned MLP proposed in this paper, based MLP best fit hyper-parameters along with feature selection is applied for breast cancer classification using Wisconsin Diagnostic Breast Cancer (WDBC) dataset. As the tuned MLP experimental result shows an accuracy reached 97.70% as it outperforms the basic MLP.



Classification, Machine Learning, Breast cancer, Multi-Layer Perceptron, Hyper-parameters, Tuning



[1] Duijm, L. E. M., Groenewoud, J. H., Jansen, F. H., Fracheboud, J., van Beek, M., & de Koning, H. J. (2004), "Mammography screening in the Netherlands: delay in the diagnosis of breast cancer after breast cancer screening", British journal of cancer, 91(10), 1795.
[2] Pendharkar, P. C., Rodger, J. A., Yaverbaum, G. J., Herman, N., & Benner, M. (1999), "Association, statistical, mathematical and neural approaches for mining breast cancer patterns", Expert Systems with Applications, 17(3), 223-232.
[3] Zheng, Y., Yu, J., Kambhamettu, C., Englander, S., Schnall, M. D., & Shen, D. (2007, October), "De-enhancing the dynamic contrast-enhanced breast MRI for robust registration", In International Conference on Medical Image Computing and Computer-Assisted Intervention (pp. 933-941), Springer, Berlin, Heidelberg.
[4] Wiki, A. I., "Artificial Intelligence (AI) vs. Machine Learning vs. Deep Learning", skymind. [Online]. Available: https://skymind.ai/wiki/ai-vs-machine-learning-vs-deep-learning.
[5] Deo, R. C., "Machine Learning in Medicine", Circulation, vol. 132, no. 20, pp. 1920–1930, Nov. 2015.
[6] Karabatak, H., "A new classifier for breast cancer detection based on Naïve Bayesian", Meas. J. Int. Meas. Confed., vol. 72, pp. 32-36, 2015.
[7] Zare-Zardini, H, "Nanotechnology and Pediatric Cancer: Prevention, Diagnosis and Treatment", Iran. J. Pediatr. Hematol. Oncol., vol. 5, no. 4, pp. 233–248, 2015.
[8] scikit-learn.org, "Neural network models (supervised)", [Online]. Available: https://scikit-learn.org/stable/modules/neural_networks_supervised.html#complexity. [Accessed: 25-Mar-2019].
[9] [NickGillian, ‘GRT: MLP’, 2014.
[10] Aggarwal, M., "Performance analysis of different feature selection methods in intrusion detection", Int. J. Sci. Technol. Res., vol. 2, no. 6, pp. 225–231, 2013.
[11] Haleh, V., & Imam Ibrahim, F., "Feature Selection Methods : Genetic Algorithms vs. Greedy-like Search", Proc. Int. Conf. Fuzzy Intell. Control Syst., vol. 1, no. Vafaie 93, pp. 1-10, 1997.
[12] Hewa, K., "An introduction to Grid Search", Data Driven Investor, 2019. [Online]. Available: https://medium.com/datadriveninvestor/an-introduction-to-grid-search-ff57adcc0998. [Accessed: 01-Jan-2019].
[13] Singh, A. V., "What is a grid search, and why do we use it in machine learning?", 2018. [Online]. Available: https://www.quora.com/What-is-a-grid-search-and-why-do-we-use-it-in-machine-learning. [Accessed: 01-Apr-2019].
[14] Suleiman, A. A., "Image Classification Based on Enhancement of Local Binary Pattern", MEU, 2018.
[15] Anemangely, M., Ramezanzadeh, A., Tokhmechi, B., Molaghab, A., & Mohammadian, A. (2018), "Drilling rate prediction from petrophysical logs and mud logging data using an optimized multilayer perceptron neural network", Journal of Geophysics and Engineering, 15(4), 1146-1159.
[16] Mohan, C., & Nagarajan, S. (2019), "An improved tree model based on ensemble feature selection for classification", Turkish Journal of Electrical Engineering & Computer Sciences, 27(2), 1290-1307.
[17] Pham, B. T., Nguyen, M. D., Bui, K. T. T., Prakash, I., Chapi, K., & Bui, D. T. (2019), "A novel artificial intelligence approach based on Multi-layer Perceptron Neural Network and Biogeography-based Optimization for predicting coefficient of consolidation of soil", Catena, 173, 302-311.
[18] Zheng, B., Yoon, S. W., & Lam, S. S. (2014), "Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms", Expert Systems with Applications, 41(4), 1476-1482.
[19] Sabri, A.T., Mohammads, A.H., Al-Shargabi, B. and Hamdeh, M.A., 2010, "Developing new continuous learning approach for spam detection using artificial neural network (CLA_ANN)", European Journal of Scientific Research, 42(3), pp. 525-535.
[20] Nilashi, M., Ibrahim, O., Ahmadi, H., & Shahmoradi, L. (2017), "A knowledge-based system for breast cancer classification using fuzzy logic method", Telematics and Informatics, 34(4), 133–144. https://doi.org/10.1016/j.tele.2017.01.007.
[21] Aljawarneh, S., Al-shargabi, B., Rashaideh H., (2013), "Gene classification: A review", In Proceedings of IEEE ICIT'2013 May 8.
[22] Al-Shargabi, B., Amro, I, Kanaan. G. (2009), "Exploit Genetic Algorithm to Enhance Arabic Information Retrieval", In 3rd International Conference on Arabic Language Processing (CITALA’09), Rabat, Morocco, May 2009.



Al-Shargabi, B., Al-Shami, F., and Alkhawaldeh, R. S. (2019). Enhancing Multi-Layer Perceptron for Breast Cancer Prediction. International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, 130, 11-20. doi: 10.33832/ijast.2019.130.02.

Al-Shargabi, Bassam, et al. “Enhancing Multi-Layer Perceptron for Breast Cancer Prediction.” International Journal of Advanced Science and Technology, ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 130, 2019, pp. 11-20. IJAST, http://article.nadiapub.com/IJAST/Vol130/2.html.

[1] B. Al-Shargabi, F. Al-Shami, and R. S. Alkhawaldeh, “Enhancing Multi-Layer Perceptron for Breast Cancer Prediction.” International Journal of Advanced Science and Technology (IJAST), ISSN: 2005-4238(Print); 2207-6360 (Online), NADIA, vol. 130, pp. 11-20, Sep. 2019.