An Ensemble Classifier Method for Breast Cancer Detection Using Genetic Algorithm and Multistage Adjustment of Weights in the MLP Neural Network
Subject Areas : Telecommunications Engineering
Amin
Rezaeipanah
1
(Department of Computer, Faculty of Computer Science, University of Rahjuyan Danesh Borazjan, Bushehr, Iran)
S. J.
Mirabedini
2
(Department of Computer Engineering-Software, Central Tehran Branch, Islamic Azad University, Tehran, Iran)
ali
mobaraki
3
(Department of Computer Engineering-Software, Bushehr Branch, Islamic Azad University, Bushehr, Iran)
Keywords:
Abstract :
Today, with the increasing spread of science, the use of decision support systems can be of great help in the therapeutic policies of the Doctor. For this purpose, the use of artificial intelligence systems in predicting and diagnosing breast cancer, which is one of the most common cancers among women, is being considered. In this study, the process of diagnosis of breast cancer is done by using multistage weights in the MLP neural network in two layers. In the first layer, the three classifiers are trained simultaneously on the learning set data. Upon completion of the training, the output of the classifier of the first layer is accumulated together with the learning set data in the new sets. This set is given as an input to the second layer superconductor, and the supra-class mapping maps between the outputs of each of the ordinary classifiers of the first layer with the actual output classes. The three-layer structure of the first layer, as well as the second-layer supraclavicle, is a MLP neural network that optimizes the weights, effective properties and the size of the hidden layer simultaneously using an innovative genetic algorithm. In order to evaluate the accuracy of the proposed model, the Wisconsin database is used, which was created by the FNA test. Experiment results on the WBCD dataset the accuracy is 98.72% for the proposed method, which is relative to GAANN, CAFS algorithms provide better performance.
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[4] C. DeSantis., J. Ma, L. Bryan and, A. Jemal, “Breast cancer statistics, 2013”, CA: a cancer journal for clinicians, vol.64, no.1, pp.52-62, 2014.
[5] K. M. Kash, J. C. Holland, M. S. Halper and, D. G. Miller, “Psychological distress and surveillance behaviors of women with a family history of breast cancer”. JNCI: Journal of the National Cancer Institute, vol.84, no.1, pp.24-30, 1992.
[6], R. L. Siegel, , K. D. Miller and, A. Jemal, “Cancer statistics, 2015”, CA: a cancer journal for clinicians, vol.65, no.1, pp.5-29, 2015.
[7] E. C. Fear, X. Li, S. C. Hagness and, M. A. Stuchly, “Confocal microwave imaging for breast cancer detection: Localization of tumors in three dimensions”, IEEE Transactions on Biomedical Engineering, vol.49, no.8, pp.812-822, 2002.
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[10] شیخ پور, راضیه و مهدی آقاصرام (1394)، انتخاب ویژگی‌های موثر در تشخیص سرطان سینه با استفاده از مدل‌های پارامتریک یادگیری ماشین. فصلنامه علمی-پژوهشی بیماری های سینه, 8(2), 16-23.
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[13] M. Nilashi, O. Ibrahim, H. Ahmadi and , L Shahmoradi, “A knowledge-based system for breast cancer classification using fuzzy logic method”, Telematics and Informatics, 34(4), pp.133-144, 2017.
[14], R. D. H. Devi, and, M. I. Devi, “Outlier detection algorithm combined with decision tree classifier for early diagnosis of breast cancer”. Int J Adv Engg Tech, vol. 6,no.2 , pp. 93-98, 2016.
[15] K. J. Wang, B. Makond, K. H. Chen, and, K. M. Wang, “A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients”. Applied Soft Computing, 2016, pp.15-24.
[16] J. Diz, G. Marreiros, and A. Freitas, “Applying Data Mining Techniques to Improve Breast Cancer Diagnosis”. Journal of medical systems, vol.40, no.9, pp. 203-210, 2016.
[17] K. Vaidehi, and, T. S. Subashini, “Breast tissue characterization using combined K-NN classifier”, Indian Journal of Science and Technology, vol.8,no.1, pp. 23-26, 2015.
[18] A. Onan, “A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer”, Expert Systems with Applications, vol.42, no.20, pp.6844-6852, 2015.
[19] R. Sheikhpour, M. A. Sarram, and, R. Sheikhpour , “Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer”, Applied Soft Computing, vol.40, pp.113-131, 2016.
[20] F. Ahmad, N. A. M. Isa, Z. Hussain, M. K. Osman, and S. N. Sulaiman, “A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer”. Pattern Analysis and Application, vol.18, no.4, pp.861-870, 2015.
[21] MM. Kabir, MM. Islam and K. Murase, “A new wrapper feature selection approach using neural network”, Neurocomputing,vol. 73, pp. 3273–3283, 2010.
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[23] Z. Kim and R. Nevatia, “Uncertain reasoning and learning for feature grouping”, Computer Vision and Image Understanding, vol.76, no.3, pp.278-288, 1999.
[24] G. Gan, and M. K. P. Ng, “Subspace clustering with automatic feature grouping” Pattern Recognition, vol.48, no.11, pp. 3703-3713, 2015.
[25] Breast Cancer Wisconsin (Original) dataset, UCI machine language repository, 1992.
[26] A. Marcano-Cedeño, J. Quintanilla-Domínguez and D. Andina, “WBCD breast cancer database classification applying artificial metaplasticity neural network”, Expert Systems with Applications, vol.38, no.8, pp. 9573-9579, 2011.
[27] MF. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis”, Expert Syst Appl, vol.36, no.2, pp. 3240–3247, 2009.
[28] Y. Peng, Z. Wu, J. Jiang, “A novel feature selection approach for biomedical data classification”, J Biomed Inform, vol.43, no.1, pp.15–23, 2010.
[29] A. Marcano-Ceden˜o, J. Quintanilla-Domı´nguez and D. Andina, “WBCD breast cancer database classification applying artificial metaplasticity neural network”, Expert Syst Appl, vol.38, no.8, pp.9573–9579, 2011.
[30] M. Karabatak, MC. Ince, “An expert system for detection of breast cancer based on association rules and neural network”, Expert Syst Appl, vol.36, no.2, pp. 3465–3469, 2009.
[31] R. Stoean, C. Stoean, “Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection” Expert Syst Appl, vol.40, no.7, pp.2677–2686, 2013.
[32], V. Chaurasia, and, S. Pal, (2017). “A novel approach for breast cancer detection using data mining techniques”, International Journal of Innovative Research in Computer and Communication Engineering, vol.2, no.1, 2017.
[33] D. H. Wolpert, “Stacked generalization”, Neural networks, vol.5, no.2, 241-259, 1992.
[34] A. Al-Ani, A. Alsukker, and R. N. Khushaba, “Feature subset selection using differential evolution and a wheel based search strategy”. Swarm and Evolutionary Computation, vol.9, pp.15-26, 2013.
[35] S. Lee, H. Park, and M. Jeon, “Binary particle swarm optimization with bit change mutation, IEICE transactions on fundamentals of electronics, communications and computer sciences, vol.90, no.10, pp.2253-2256, 2007.
[36] G. Tsoumakas, I. Katakis and I. Vlahavas, “Effective voting of heterogeneous classifiers”. in European Conference on Machine Learning, 2004, pp. 465-476.
_||_[1] A. Antoniou, P. D. P. Pharoah, S. Narod, H. A. Risch, J. E. Eyfjord, J. L. Hopper and B. Pasini, “Average risks of breast and ovarian cancer associated with BRCA1 or BRCA2 mutations detected in case series unselected for family history: a combined analysis of 22 studies”, The American Journal of Human Genetics, vol.72, no.5, pp.1117-1130, 2003.
[2] L. C. Hartmann, D. J. Schaid, J. E. Woods, T. P. Crotty, J. L. Myers, P. G. Arnold and, M. H. Frost, “Efficacy of bilateral prophylactic mastectomy in women with a family history of breast cancer”, New England Journal of Medicine, vol.340no. 2, pp.77-84, 1999.
[3], M. C. King, , J. H. Marks and J. B. Mandell, “Breast and ovarian cancer risks due to inherited mutations in BRCA1 and BRCA2”, Science, vol.302, no.5645, pp.643-646, 2003.
[4] C. DeSantis., J. Ma, L. Bryan and, A. Jemal, “Breast cancer statistics, 2013”, CA: a cancer journal for clinicians, vol.64, no.1, pp.52-62, 2014.
[5] K. M. Kash, J. C. Holland, M. S. Halper and, D. G. Miller, “Psychological distress and surveillance behaviors of women with a family history of breast cancer”. JNCI: Journal of the National Cancer Institute, vol.84, no.1, pp.24-30, 1992.
[6], R. L. Siegel, , K. D. Miller and, A. Jemal, “Cancer statistics, 2015”, CA: a cancer journal for clinicians, vol.65, no.1, pp.5-29, 2015.
[7] E. C. Fear, X. Li, S. C. Hagness and, M. A. Stuchly, “Confocal microwave imaging for breast cancer detection: Localization of tumors in three dimensions”, IEEE Transactions on Biomedical Engineering, vol.49, no.8, pp.812-822, 2002.
[8] A. Asuncion and Newman, DUCI machine learning repository, (2007).
[9] مرزوقی, فاطمه و علی اصغر صفائی (۱۳۹۵)، مدلی برای تشخیص سرطان سینه مبتنی بر شبکه های عصبی، کنفرانس بین المللی مهندسی کامپیوتر و فناوری اطلاعات، تهران، دبیرخانه دایمی کنفرانس.
[10] شیخ پور, راضیه و مهدی آقاصرام (1394)، انتخاب ویژگی‌های موثر در تشخیص سرطان سینه با استفاده از مدل‌های پارامتریک یادگیری ماشین. فصلنامه علمی-پژوهشی بیماری های سینه, 8(2), 16-23.
[11] بهمن یار, حسن و بهزاد یثربی (۱۳۹۶)، انتخاب ویژگی های موثر برای تشخیص سرطان سینه با استفاده از الگوریتم شبکه عصبی مصنوعی با رویکرد تکاملی، کنفرانس ملی پژوهش های نوین در برق، کامپیوتر و مهندسی پزشکی، کازرون، دانشگاه آزاد اسلامی واحد کازرون.
[12] سندی, فاطمه؛ الهام عسکری؛ نرجس مطهری و پرستو شهابی چروده (۱۳۹۵)، مقایسه و ارزیابی تکنیک های داده کاوی در جهت تشخیص بهتر سرطان سینه، دومین کنفرانس بین المللی مدیریت و فناوری اطلاعات و ارتباطات، تهران، شرکت خدمات برتر.
[13] M. Nilashi, O. Ibrahim, H. Ahmadi and , L Shahmoradi, “A knowledge-based system for breast cancer classification using fuzzy logic method”, Telematics and Informatics, 34(4), pp.133-144, 2017.
[14], R. D. H. Devi, and, M. I. Devi, “Outlier detection algorithm combined with decision tree classifier for early diagnosis of breast cancer”. Int J Adv Engg Tech, vol. 6,no.2 , pp. 93-98, 2016.
[15] K. J. Wang, B. Makond, K. H. Chen, and, K. M. Wang, “A hybrid classifier combining SMOTE with PSO to estimate 5-year survivability of breast cancer patients”. Applied Soft Computing, 2016, pp.15-24.
[16] J. Diz, G. Marreiros, and A. Freitas, “Applying Data Mining Techniques to Improve Breast Cancer Diagnosis”. Journal of medical systems, vol.40, no.9, pp. 203-210, 2016.
[17] K. Vaidehi, and, T. S. Subashini, “Breast tissue characterization using combined K-NN classifier”, Indian Journal of Science and Technology, vol.8,no.1, pp. 23-26, 2015.
[18] A. Onan, “A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer”, Expert Systems with Applications, vol.42, no.20, pp.6844-6852, 2015.
[19] R. Sheikhpour, M. A. Sarram, and, R. Sheikhpour , “Particle swarm optimization for bandwidth determination and feature selection of kernel density estimation based classifiers in diagnosis of breast cancer”, Applied Soft Computing, vol.40, pp.113-131, 2016.
[20] F. Ahmad, N. A. M. Isa, Z. Hussain, M. K. Osman, and S. N. Sulaiman, “A GA-based feature selection and parameter optimization of an ANN in diagnosing breast cancer”. Pattern Analysis and Application, vol.18, no.4, pp.861-870, 2015.
[21] MM. Kabir, MM. Islam and K. Murase, “A new wrapper feature selection approach using neural network”, Neurocomputing,vol. 73, pp. 3273–3283, 2010.
[22] G. L. Scott, and H. C. Longuet-Higgins, “Feature grouping by'relocalisation'of eigenvectors of the proximity matrix”. in BMVC,1990, pp. 1-6.
[23] Z. Kim and R. Nevatia, “Uncertain reasoning and learning for feature grouping”, Computer Vision and Image Understanding, vol.76, no.3, pp.278-288, 1999.
[24] G. Gan, and M. K. P. Ng, “Subspace clustering with automatic feature grouping” Pattern Recognition, vol.48, no.11, pp. 3703-3713, 2015.
[25] Breast Cancer Wisconsin (Original) dataset, UCI machine language repository, 1992.
[26] A. Marcano-Cedeño, J. Quintanilla-Domínguez and D. Andina, “WBCD breast cancer database classification applying artificial metaplasticity neural network”, Expert Systems with Applications, vol.38, no.8, pp. 9573-9579, 2011.
[27] MF. Akay, “Support vector machines combined with feature selection for breast cancer diagnosis”, Expert Syst Appl, vol.36, no.2, pp. 3240–3247, 2009.
[28] Y. Peng, Z. Wu, J. Jiang, “A novel feature selection approach for biomedical data classification”, J Biomed Inform, vol.43, no.1, pp.15–23, 2010.
[29] A. Marcano-Ceden˜o, J. Quintanilla-Domı´nguez and D. Andina, “WBCD breast cancer database classification applying artificial metaplasticity neural network”, Expert Syst Appl, vol.38, no.8, pp.9573–9579, 2011.
[30] M. Karabatak, MC. Ince, “An expert system for detection of breast cancer based on association rules and neural network”, Expert Syst Appl, vol.36, no.2, pp. 3465–3469, 2009.
[31] R. Stoean, C. Stoean, “Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection” Expert Syst Appl, vol.40, no.7, pp.2677–2686, 2013.
[32], V. Chaurasia, and, S. Pal, (2017). “A novel approach for breast cancer detection using data mining techniques”, International Journal of Innovative Research in Computer and Communication Engineering, vol.2, no.1, 2017.
[33] D. H. Wolpert, “Stacked generalization”, Neural networks, vol.5, no.2, 241-259, 1992.
[34] A. Al-Ani, A. Alsukker, and R. N. Khushaba, “Feature subset selection using differential evolution and a wheel based search strategy”. Swarm and Evolutionary Computation, vol.9, pp.15-26, 2013.
[35] S. Lee, H. Park, and M. Jeon, “Binary particle swarm optimization with bit change mutation, IEICE transactions on fundamentals of electronics, communications and computer sciences, vol.90, no.10, pp.2253-2256, 2007.
[36] G. Tsoumakas, I. Katakis and I. Vlahavas, “Effective voting of heterogeneous classifiers”. in European Conference on Machine Learning, 2004, pp. 465-476.
