Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data

Authors

DOI:

https://doi.org/10.18201/ijisae.2018648455

Keywords:

Breast cancer, Artificial Neural Network, Extreme Learning Machine, Support Vector Machine, K-Nearest Neighbors, Hyperparameter Optimization

Abstract

Today, one of the most common types of cancer is breast cancer. It is crucial to prevent the propagation of malign cells to reduce the rate of cancer induced mortality. Cancer detection must be done as early as possible for this purpose. Machine Learning techniques are used to diagnose or predict the success of treatment in medicine. In this study, four different machine learning algorithms were used to early detection of breast cancer. The aim of this study is to process the results of routine blood analysis with different ML methods and to understand how effective this method is for detection. Methods used can be listed as Artificial Neural Network (ANN), standard Extreme Learning Machine (ELM), Support Vector Machine (SVM) and K-Nearest Neighbor (k-NN). Dataset used were taken from UCI library. In this dataset age, body mass index (BMI), glucose, insulin, homeostasis model assessment (HOMA), leptin, adiponectin, resistin and chemokine monocyte chemoattractant protein 1 (MCP1)   attributes were used. Parameters that have the best accuracy values were found by using four different Machine Learning techniques. For this purpose, hyperparameter optimization method was used. In the end, the results were compared and discussed.

Downloads

Download data is not yet available.

Author Biographies

Muhammet Fatih Aslan, Karamanoğlu Mehmetbey University

Engineering Faculty, Department of Electrical and Electronics Engineering

Yunus Celik, Karamanoğlu Mehmetbey University

Engineering Faculty, Department of Electrical and Electronics Engineering

Kadir Sabanci, Karamanoğlu Mehmetbey University

Engineering Faculty, Department of Electrical and Electronics Engineering

Akif Durdu, Konya Technical University

Engineering and Natural Sciences Faculty, Department of Electrical and Electronics Engineering

References

J. Tang, R. M. Rangayyan, J. Xu, I. El Naqa, and Y. Yang, “Computer-aided detection and diagnosis of breast cancer with mammography: recent advances,” IEEE Transactions on Information Technology in Biomedicine, vol. 13, no. 2, pp. 236-251, 2009.

Z. Ahmad, A. Khurshid, A. Qureshi, R. Idress, N. Asghar, and N. Kayani, “Breast carcinoma grading, estimation of tumor size, axillary lymph node status, staging, and nottingham prognostic index scoring on mastectomy specimens,” Indian Journal of Pathology and Microbiology, vol. 52, no. 4, pp. 477, 2009.

U. R. Acharya, E. Y.-K. Ng, J.-H. Tan, and S. V. Sree, “Thermography based breast cancer detection using texture features and support vector machine,” Journal of medical systems, vol. 36, no. 3, pp. 1503-1510, 2012.

K. Ganesan, U. R. Acharya, C. K. Chua, L. C. Min, K. T. Abraham, and K.-H. Ng, “Computer-aided breast cancer detection using mammograms: a review,” IEEE Reviews in biomedical engineering, vol. 6, pp. 77-98, 2013.

WHO. “Breast cancer: prevention and control,” http://www.who.int/cancer/detection/breastcancer/en/index1.html.

D. Bazazeh, and R. Shubair, “Comparative study of machine learning algorithms for breast cancer detection and diagnosis.” 5th International Conference on Electronic Devices, Systems and Applications (ICEDSA), pp. 1-4, 2016.

M. Hejmadi, Introduction to cancer biology, 2 ed.: Bookboon, 2009.

I. Schreer, and J. Lüttges, “Breast cancer: early detection,” Radiologic-Pathologic Correlations from Head to Toe, pp. 767-784: Springer, 2005.

B. K. Gayathri, and P. Raajan, “A survey of breast cancer detection based on image segmentation techniques.” IEEE International Conference on Computing Technologies and Intelligent Data Engineering (ICCTIDE'16), pp. 1-5, 2016.

P. Král, and L. Lenc, “LBP features for breast cancer detection.” IEEE International Conference on Image Processing (ICIP), pp. 2643-2647, 2016.

P. Louridas and C. Ebert, “Machine Learning,” IEEE Software, vol. 33, no. 5, pp. 110-115, 2016.

H. Asri, H. Mousannif, H. Al Moatassime, and T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis,” Procedia Computer Science, vol. 83, pp. 1064-1069, 2016.

K. P. Bennett, and J. A. Blue, “A support vector machine approach to decision trees.” IEEE International Joint Conference on Neural Networks Proceedings. IEEE World Congress on Computational Intelligence, pp. 2396-2401, 1998.

B. Zheng, S. W. Yoon, and S. S. Lam, “Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms,” Expert Systems with Applications, vol. 41, no. 4, pp. 1476-1482, 2014.

R. Alyami, J. Alhajjaj, B. Alnajrani, I. Elaalami, A. Alqahtani, N. Aldhafferi, T. O. Owolabi, and S. O. Olatunji, “Investigating the effect of Correlation based Feature Selection on breast cancer diagnosis using Artificial Neural Network and Support Vector Machines.” IEEE International Conference on Informatics, Health & Technology (ICIHT), pp. 1-7, 2017.

M. Hussain, S. K. Wajid, A. Elzaart, and M. Berbar, “A comparison of SVM kernel functions for breast cancer detection.” IEEE Eighth International Conference Computer Graphics, Imaging and Visualization, pp. 145-150, 2011

W. Cherif, “Optimization of K-NN algorithm by clustering and reliability coefficients: application to breast-cancer diagnosis,” Procedia Computer Science, vol. 127, pp. 293-299, 2018.

W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu, “Machine Learning with Applications in Breast Cancer Diagnosis and Prognosis,” Designs, vol. 2, no. 2, pp. 13, 2018.

P. Suryachandra, and P. V. S. Reddy, “Comparison of machine learning algorithms for breast cancer.” IEEE International Conference on Inventive Computation Technologies (ICICT), pp. 1-6, 2016.

H. Jouni, M. Issa, A. Harb, G. Jacquemod, and Y. Leduc, “Neural Network architecture for breast cancer detection and classification.” IEEE International Multidisciplinary Conference on Engineering Technology (IMCET), pp. 37-41, 2016.

UCI. “Machine Learning Repository,” https://archive.ics.uci.edu/ml/index.php.

M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, R. Seiça, and F. Caramelo, “Using Resistin, glucose, age and BMI to predict the presence of breast cancer,” BMC cancer, vol. 18, no. 1, pp. 29, 2018.

J. Crisóstomo, P. Matafome, D. Santos-Silva, A. L. Gomes, M. Gomes, M. Patrício, L. Letra, A. B. Sarmento-Ribeiro, L. Santos and R. Seiça, “Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer,” International Journal of Basic and Clinical Endocrinology, vol. 53, no. 2, pp. 433-442, 2016.

P. Kshirsagar, and N. Rathod, “Artificial neural network,” International Journal of Computer Applications, 2012.

N. Gupta, “Artificial neural network,” Network and Complex Systems, vol. 3, no. 1, pp. 24-28, 2013.

G.-B. Huang, Q.-Y. Zhu, and C.-K. Siew, “Extreme learning machine: a new learning scheme of feedforward neural networks.” IEEE International Joint Conference on Neural Networks, pp. 985-990, 2004.

Y. Yang, and Q. M. J. Wu, “Extreme Learning Machine With Subnetwork Hidden Nodes for Regression and Classification,” IEEE Transactions on Cybernetics, vol. 46, no. 12, pp. 2885-2898, 2016.

S. Ding, H. Zhao, Y. Zhang, X. Xu, and R. Nie, “Extreme learning machine: algorithm, theory and applications,” Artificial Intelligence Review, vol. 44, no. 1, pp. 103-115, 2015.

N.-Y. Liang, G.-B. Huang, P. Saratchandran, and N. Sundararajan, “A fast and accurate online sequential learning algorithm for feedforward networks,” IEEE Transactions on neural networks, vol. 17, no. 6, pp. 1411-1423, 2006.

V. N. Mandhala, V. Sujatha, and B. R. Devi, “Scene classification using support vector machines.” IEEE International Conference on Advanced Communications, Control and Computing Technologies, pp. 1807-1810, 2014.

M. E. Mavroforakis, and S. Theodoridis, "Support Vector Machine (SVM) classification through geometry.", IEEE 13th European Signal Processing Conference, pp. 1-4, 2005.

W.-C. Lai, P.-H. Huang, Y.-J. Lee, and A. Chiang, “A distributed ensemble scheme for nonlinear support vector machine,” IEEE Tenth International Conference on Intelligent Sensors, Sensor Networks and Information Processing (ISSNIP), pp. 1-6, 2015.

N. Suguna, and K. Thanushkodi, “An improved k-nearest neighbor classification using genetic algorithm,” International Journal of Computer Science Issues, vol. 7, no. 2, pp. 18-21, 2010.

J. Kim¹, B.-S. Kim, and S. Savarese, “Comparing image classification methods: K-nearest-neighbor and support-vector-machines,” Proceedings of the 6th WSEAS international conference on Computer Engineering and Applications, and Proceedings of the 2012 American conference on Applied Mathematics, vol. 1001, pp. 48109-2122, 2012.

Downloads

Published

27.12.2018

How to Cite

Aslan, M. F., Celik, Y., Sabanci, K., & Durdu, A. (2018). Breast Cancer Diagnosis by Different Machine Learning Methods Using Blood Analysis Data. International Journal of Intelligent Systems and Applications in Engineering, 6(4), 289–293. https://doi.org/10.18201/ijisae.2018648455

Issue

Section

Research Article

Most read articles by the same author(s)