Using Combined Classifier Based on the Separation of Conventional and Unconventional Samples to Diagnose Breast Cancer
Subject Areas : electrical and computer engineeringamin rezaeipanah 1 * , hesam vaghebin 2
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Abstract :
Breast cancer is one of the most common types of cancers in women and in recent years there has been a significant increase in the number of people with this disease. With the increasing spread of science, data mining has become one of the most widely used areas for improving therapeutic systems. In this paper, the diagnosis of breast cancer is performed in two steps. In the first step, an improved genetic algorithm is used to identify the desirable features in the prediction of this disease, and in the second stage, conventional and Unconventional samples are identified to increase the accuracy and create the final classification model. For classification work, a comparison between two decision tree and Support vector machine model is used to show the results of the superiority of the Support vector machine model. The results of the experiments reported the accuracy of breast cancer diagnosis on WBCD, WDBC and WPBC data sets are 99.26%, 98.55% and 98.45%, respectively.
[1] A. G. Freifeld, et al., "Clinical practice guideline for the use of antimicrobial agents in neutropenic patients with cancer: 2010 update by the Infectious Diseases Society of America," Clinical Infectious Diseases, vol. 52, no. 4, pp. 56-93, Feb. 2011.
[2] K. J. Cios and G. W. Moore, "Uniqueness of medical data mining," Artificial Intelligence in Medicine, vol. 26, no. 1, pp. 1-24, Sept. 2002.
[3] R. Shen, Y. Yang, and F. Shao, "Intelligent breast cancer prediction model using data mining techniques," in Proc.6th Int. Conf. on Intelligent Human-Machine Systems and Cybernetics, IHMSC’14, pp. 384-387, Hangzhou, China, 26-27 Aug. 2014.
[4] S A. R. M. Al-shamasneh, ans U. H. B. Obaidellah, "Artificial intelligence techniques for cancer detection and classification: review study," European Scientific Journal, vol. 13, no. 3, pp. 342-370, Jan. 2017.
[5] A. K. Sampath and N. Gomathi, "Probabilistic model based hybrid classifier for character recognition," International J. of Uncertainty, Fuzziness and Knowledge-Based Systems, vol. 25, no. 4, pp. 621-647, Aug. 2017.
[6] م. عبدالرزاق‌نژاد، "طبقه‌بندی و شناسایی وب‌سایت‌های فیشینگ به کمک مجموعه قوانین فازی و الگوریتم اصلاح‌شده بهینه‌سازی صفحات شیب‌دار،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، ب- مهندسی کامپیوتر، جلد 14، شماره 3، صص. 321-311، پاییز 1395.
[7] ه. صدوقی یزدی، ع. محی‌الدینی شاهم‌آبادی‌پور و م. خادمی، "طبقه‌بند خودسازمانده هندسی مبتنی بر یادگیری فعال برای نهان‌کاوی در محیط ویدئو با صرف حداقل برچسب،" نشریه مهندسی برق و مهندسی کامپیوتر ایران، ب- مهندسی کامپیوتر، جلد 16، شماره 1، صص. 40-28، بهار 1397.
[8] ز. مروج و ج. آذرخش، "شبیه‌سازی و طبقه‌بندی وقایع کیفیت توان با استفاده از شبکه عصبی،" فصلنامه مدل‌سازی در مهندسی، جلد 13، شماره 41، صص. 137-146، تابستان 1394.
[9] ر. شیخ‌پور و م. آقاصرام، "انتخاب ویژگی‌های مؤثر در تشخیص سرطان سینه با استفاده از مدل‌های پارامتریک یادگیری ماشین،" فصلنامه علمي- پژوهشي بيماري‌هاي سینه، جلد 8، شماره 2، صص. 16-23، تابستان 1394.
[10] ا. صادقی‌پور، ن. ا. صحراگرد، م. ر. سایبانی و ز. بهمن‌زاده، "تشخیص سرطان سینه بر اساس رویکرد ترکیبی مبتنی بر الگوریتم کرم شب‌تاب و ترکیب سیستم‌های هوشمند،" مجموعه مقالات کنفرانس بین‌المللی مهندسی، ICOAC، هنر و محیط زیست، کشور لهستان، صص. 31-24، پاییز 1393.
[11] S. K. Mandal, "Performance analysis of data mining algorithms for breast cancer cell detection using Naive Bayes, logistic regression and decision tree," International J. of Engineering and Computer Science, vol. 6, no. 2, pp. 20388-20391, Feb. 2017.
[12] 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, Nov. 2015.
[13] B. G. Patil and S. N. Jain, "Cancer cells detection using digital image processing methods," International J. of Latest Trends in Engineering and Technology, vol. 3, no. 4, pp. pp. 45-49, Mar.2014.
[14] M. Nilashi, O. Ibrahim, H. Ahmadi, and L. Shahmoradi, "A knowledge-based system for breast cancer classification using fuzzy logic method," Telematics and Informatics, vol. 34, no. 4, pp. 133-144, Jul. 2017. [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, vol. 20, pp. 15-24, Jul. 2016.
[16] R. D. H. Devi and M. I. Devi, "Outlier detection algorithm combined with decision tree classifier for early diagnosis of breast cancer," International Journal of Advanced Engineering Technology, vol. 93, no. 2, pp. 93-98, Apr. 2016.
[17] J. Diz, G. Marreiros, and A. Freitas, "Applying data mining techniques to improve breast cancer diagnosis," J. of Medical Systems, vol. 40, no. 9, pp. 203-209, Aug. 2016.
[18] S. Ghosh, S. Mondal, and B. Ghosh, "A comparative study of breast cancer detection based on SVM and MLP BPN classifier," in 1st IEEE Int. Conf. on Automation, Control, Energy and Systems, ACES’14, 4 pp., Hooghy, India, 1-2 Feb. 2014.
[19] K. Vaidehi and T. S. Subashini, "Breast tissue characterization using combined K-NN classifier," Indian J. of Science and Technology, vol. 8, no. 1, pp. 23-26, Jan. 2015.
[20] 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, no. C, pp. 113-131, Mar. 2016.
[21] 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 Applications, vol. 18, no. 4, pp. 861-870, Nov. 2015.
[22] M. A. Hall, "Correlation-based feature selection of discrete and numeric class machine learning," in Proc. of the 17th Int.Conf. on Machine Learning, ICML'00, pp. 359-366, 29 Jun.-2 Jul. 2000.
[23] J. L. J. Laredo, S. S. Nielsen, G. Danoy, P. Bouvry, and C. M. Fernandes, "Cooperative selection: improving tournament selection via altruism," in Proc. European Conf. on Evolutionary Computation in Combinatorial Optimization, pp. 85-96, Apr. 2014.
[24] M. Bulmer, "The effect of selection on genetic variability," Tthe American Naturalist, vol. 105, no. 943, pp. 201-211, May 1971.
[25] M. Tavallaee, N. Stakhanova, and A. A. Ghorbani, "Towards credible evaluation of anomaly based intrusion detection methods," IEEE Trans. on System, Man and Cybernetics, Part C, Applications and Reviews, vol. 40, no. 5, pp. 516-524, Sept. 2010.
[26] Breast Cancer Wisconsin (Original) dataset, UCI Machine Language Repository, 1992.
[27] س. زنگنه، ر. جوانمرد، ع. تپه و م. م. عبادزاده، "رویکرد ترکیبی برای کاهش ابعاد ویژگی‌های مجموعه‌های داده‌ای با استفاده از الگوریتم ترکیبی شبکه عصبی و الگوریتم ژنتیک در تشخیص پزشکی،" مجموعه مقالات سومین کنفرانس داده‌کاوی، صص. 46-36، تهران، پاييز 1388.
[28] G. I. Salama, M. B. Abdelhalim, and M. A. Zeid, "Experimental comparison of classifiers for breast cancer diagnosis," in Proc. 7th IEEE Int. Conf. on Computer Engineering & Systems, ICCES’12, pp. 180-185, Cairo, Egypt, 27-29 Nov.2012.