تشخیص سرطان سینه با رویکرد متوازنسازی مجموعه دادهها
الموضوعات : electrical and computer engineering
1 - دانشكده مهندسی، مرکز آموزش عالی محلات، محلات، ایران
الکلمات المفتاحية: دادههای نامتوازن, نمونهبرداری فزاینده, oversampling, تشخیص خودکار بیماری.,
ملخص المقالة :
یکی از چالشهای بزرگ در تشخیص خودکار بیماریها، وجود مجموعه دادههای نامتوازن است. عدم توازن در کلاسهای داده، باعث شکست در تشخیص صحیح بیماری توسط سیستمهای تشخیصی میشود. این پژوهش الگوریتم جدیدی برای انتخاب و متوازن سازی نمونهها پیشنهاد داده که بر پایه الگوریتم ReliefF، یک الگوریتم انتخاب ویژگی، است. در الگوریتم پیشنهادی، ابتدا نمونهها بر اساس شاخص مشابهت با نمونههای همکلاسی و کلاس مخالف، وزندهی میشوند. پس از رتبهبندی نمونهها بر اساس وزن آنها، مجموعه داده با استفاده از روش نمونهبرداری فزایندهمتوازن میشود. الگوریتم ارائه شده توانایی کار با مجموعه دادههای چند کلاسه و انواع دادههای رشتهای و عددی و وجود مقادیر مفقود را دارد. علاوه بر این، به دلیل امکان انجام محاسبات به طور موازی برای هر نمونه، سربار محاسباتی کمتری نسبت به سایر الگوریتمهای متوازنسازی دارد. این الگوریتم میتواند دادهها را به طور کامل متوازن کرده و نمونههای با اهمیت را تکثیر کند. الگوریتم پیشنهادی روی سه مجموعه سرطان سینه ویسکانسین (WBCD)، مجموعه تشخیصی سرطان سینه ویسکانسین (WDBCD) و مجموعه سرطان سینه SEER اجرا شده است و سپس مجموعههای متوازن شده با الگوریتمهای مختلف طبقهبندی شدند. نتایج طبقهبندی نشاندهنده کارایی روش پیشنهادی و افزایش صحت تشخیص بیماری هستند.
المصادر:
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[2] F. Bray, et al., "Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries," CA: A Cancer Journal for Clinicians, vol. 68, no. 6, pp. 394-424. Nov 2018.
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[4] H. Parvin, B. Minaei-Bidgoli, and H. Alinejad-Rokny, "A new imbalanced learning and dictions tree method for breast cancer diagnosis," Journal of Bionanoscience, vol. 7, no. 6, pp. 673-678, 2013.
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[6] A. Anand, G. Pugalenthi, G. B. Fogel, and P. N. Suganthan, "An approach for classification of highly imbalanced data using weighting and undersampling," Amino Acids, vol. 39, no. 5, pp. 1385-1391, Nov. 2010.
[7] L. Yijing, G. Haixiang, L. Xiao, L. Yanan, and L. Jinling, "Adapted ensemble classification algorithm based on multiple classifier system and feature selection for classifying multi-class imbalanced data," Knowledge-Based Systems, vol. 94, pp. 88-104, Feb. 2016.
[8] Y. Liu, H. T. Loh, and A. Sun, "Imbalanced text classification: A term weighting approach," Expert systems with Applications, vol. 36, no. 1, pp. 690-701, Jan. 2009.
[9] C. Phua, D. Alahakoon, and V. Lee, "Minority report in fraud detection: classification of skewed data," ACM Sigkdd Explorations Newsletter, vol. 6, no. 1, pp. 50-59, 2004.
[10] B. W. Yap, et al., "An application of oversampling, undersampling, bagging and boosting in handling imbalanced datasets," in Proc. of the First Int. Conf. on Advanced Data and Information Engineering, pp. 13-22, Kuala Lumpur, Malaysia, 16-18 Dec. 2014.
[11] S. Kotsiantis, D. Kanellopoulos, and P. Pintelas, "Handling imbalanced datasets: A review," GESTS International Trans. on Computer Science and Engineering, vol. 30, pp. 25-36, 2006.
[12] N. V. Chawla, "Data mining for imbalanced datasets: An overview," In: Maimon, O., Rokach, L. (eds) Data Mining and Knowledge Discovery Handbook, pp. 875-886, Springer: Boston, 2005.
[13] M. Zięba, J. M. Tomczak, M. Lubicz, and J. Świątek, "Boosted SVM for extracting rules from imbalanced data in application to prediction of the post-operative life expectancy in the lung cancer patients," Applied Soft Computing, vol. 14, pt. A, pp. 99-108, Jan. 2014.
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[15] M. M. Rahman and D. N. Davis, "Addressing the class imbalance problem in medical datasets," International Journal of Machine Learning and Computing, vol. 3, no. 2, pp. 224-228, Apr. 2013.
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[25] M. khanna, L. k. Singh, k. Shrivastava, and R. singh, "An enhanced and efficient approach for feature selection for chronic human disease prediction: A breast cancer study," Heliyon, vol. 10, no. 5, Article ID: e26799, Mar. 2024.
[26] R. R. Kadhim and M. Y. Kamil, "Comparison of breast cancer classification models on Wisconsin dataset," Int. J. Reconfigurable Embed. Syst, vol. 11, no. 2, pp. 166-174, Jul. 2022.
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[28] H. Ouifak and A. Idri, "On the performance and interpretability of Mamdani and Takagi-Sugeno-Kang based neuro-fuzzy systems for medical diagnosis," Scientific African, vol. 20, Article ID: e01610, Jul .2023.
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[37] M. Robnik-Šikonja and I. Kononenko, "Theoretical and empirical analysis of ReliefF and RReliefF," Machine Learning, vol. 53, no. 1-2, pp. 23-69, 2003.
[38] Z. Abbasi and M. Rahmani, "An instance selection algorithm based on ReliefF," International Journal on Artificial Intelligence Tools, vol. 28, no. 1, Article ID: 1950001, 2019.
[39] Z. Wang, X. Ning, and M. Blaschko, "Jaccard metric losses: Optimizing the jaccard index with soft labels," in Proc. 37th Int. Conf. on Neural Information Processing Systems, pp. 75259-75285, New Orleans, LA, USA ,10-16 Dec. 2023.
[40] S. Bhowmick and A. Saha, "Enhancing the performance of kNN for glass identification dataset using inverse distance weight, ReliefF ranking and SMOTE," in Proc. 13th Int. Conf. on Material Processing and Characterization, pp. , Hyderabad, India, 22-24 Apr. 2022, https:// https://doi.org/10.1063/5.0161083
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[42] F. Rosenblatt, Principles of Neurodynamics. Perceptrons and the Theory of Brain Mechanisms, Spartan Books, Washington DC, US, 1961.
[43] L. Breiman, "Random forests," Machine learning, vol. 45, no. 1, pp. 5-32, Oct. 2001.
[44] Y. Freund and R. E. Schapire, "A decision-theoretic generalization of on-line learning and an application to boosting," Journal of computer and system sciences, vol. 55, no. 1, pp. 119-139, Aug. 1997.
[45] L. Breiman, "Bagging predictors," Machine learning , vol. 24, no. 2, pp. 123-140, Aug. 1996.
[46] T. M. Cover and P. E. Hart, "Nearest neighbor pattern classification," IEEE Trans. on Information Theory , vol. 13, no. 1, pp. 21-27, Jan. 1967.
[47] D. Lavanya and K. U. Rani, "Performance evaluation of decision tree classifiers on medical datasets," International Journal of Computer Applications, vol. 26, no. 4, pp. 1-4, 2011.
[48] I. Rish, "An empirical study of the naive Bayes classifier," in Proc. IJCAI 2001 Workshop on Empirical Methods in Artificial Intelligence, pp. 41-46, Seattle, Washington, USA, 4-6 Aug. 2001.
[49] J. G. Cleary and L. E. Trigg, "K*: An instance-based learner using an entropic distance measure," in Proc. of the 12th Int. Conf. on Machine Learning, pp. 108-114, Tahoe City, CA, USA, 9-12 Jul. 1995.
[50] J. R. Quinlan, C4.5: Programs for Machine Learning, Morgan Kaufmann Publishers, 1993.
[51] J. Kaur and A. Kumar, "Speech emotion recognition using CNN, k-NN, MLP and random forest," In: S. Smys, R. Palanisamy, Á.Rocha, and G. N. Beligiannis, (eds) Computer Networks and Inventive Communication Technologies. Lecture Notes on Data Engineering and Communications Technologies, vol 58. pp 499-509, Springer, Singapore, 2021.
[52] P. C. Sen, M. Hajra, and M. Ghosh, "Supervised classification algorithms in machine learning: A survey and review," In: J. Mandal and D. Bhattacharya, (eds) Emerging Technology in Modelling and Graphics. Advances in Intelligent Systems and Computing, vol 937, 99-111, Springer, Singapore.
[53] F. Osisanwo, et al., "Supervised machine learning algorithms: classification and comparison," International Journal of Computer Trends and Technology, vol. 48, no. 3, pp. 128-138, Jun. 2017.
[54] Q. Gu, L. Zhu and Z. Cai, "Evaluation measures of the classification performance of imbalanced data sets," in Proc. Int. Symp. on Intelligence Computation and Applications, pp. 461-471, Huangshi, China, 23-25 Oct. 2009.
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