Volume 21 , Issue 1 , PP: 162-173, 2023 | Cite this article as | XML | Html | PDF | Full Length Article
Rosita Elizabeth O. Torres 1 * , Jhonny Rodríguez Gutiérrez 2 , Alex G. Lara Jacome 3
Doi: https://doi.org/10.54216/IJNS.210115
Approximately one in eight women will get breast cancer in their lifetime. Because of the risks associated with radiation exposure, various women choose to avoid getting detected with breast cancer. Non-invasive breast cancer detection methods have limitations concerning the safety of radiation exposure and the accuracy with which tumors in the breast are diagnosed. Machine learning methods are commonly used to diagnose breast cancer. This paper applied three different machine learning methods like KNN, Naïve Bayes, and ID3. These methods are applied to the Wisconsin Breast Cancer dataset. In the process of categorization, data with unbalanced classes is problematic because methods are more probable to categorize fresh observations to the majority class since the likelihood of cases forming the plurality class is considerably high. So neutrosophic set is used to overcome the vague and uncertain data. This paper used single-valued neutrosophic numbers to evaluate the criteria. This paper used ROC and accuracy to evaluate the methods. The KNN has a 96.7%, Naïve Bayes has a 95.2%, and ID3 has a 95.3% accuracy.
Naï , ve Bayes , Neutrosophic Set , KNN , ID3 , Breast Cancer. ,
[1] 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, p. 13, 2018.
[2] M. Amrane, S. Oukid, I. Gagaoua, and T. Ensari, “Breast cancer classification using machine learning,” in 2018 electric electronics, computer science, biomedical engineerings’ meeting (EBBT), IEEE, 2018, pp. 1–4.
[3] H. Asri, H. Mousannif, H. Al Moatassime, and T. Noel, “Using machine learning algorithms for breast cancer risk prediction and diagnosis,” Procedia Comput. Sci., vol. 83, pp. 1064–1069, 2016.
[4] M. D. Ganggayah, N. A. Taib, Y. C. Har, P. Lio, and S. K. Dhillon, “Predicting factors for survival of breast cancer patients using machine learning techniques,” BMC Med. Inform. Decis. Mak., vol. 19, pp. 1–17, 2019.
[5] D. A. Omondiagbe, S. Veeramani, and A. S. Sidhu, “Machine learning classification techniques for breast cancer diagnosis,” in IOP Conference Series: Materials Science and Engineering, IOP Publishing, 2019, p. 12033.
[6] E. H. Houssein, M. M. Emam, A. A. Ali, and P. N. Suganthan, “Deep and machine learning techniques for medical imaging-based breast cancer: A comprehensive review,” Expert Syst. Appl., vol. 167, p. 114161, 2021.
[7] A. R. Vaka, B. Soni, and S. Reddy, “Breast cancer detection by leveraging Machine Learning,” Ict Express, vol. 6, no. 4, pp. 320–324, 2020.
[8] J. Wu and C. Hicks, “Breast cancer type classification using machine learning,” J. Pers. Med., vol. 11, no. 2, p. 61, 2021.
[9] E. A. Bayrak, P. Kırcı, and T. Ensari, “Comparison of machine learning methods for breast cancer diagnosis,” in 2019 Scientific meeting on electrical-electronics & biomedical engineering and computer science (EBBT), IEEE, 2019, pp. 1–3.
[10] P. Biswas, S. Pramanik, and B. C. Giri, “Neutrosophic TOPSIS with group decision making,” fuzzy multi-criteria Decis. using neutrosophic sets, pp. 543–585, 2019.
[11] R. M. Zulqarnain, X. L. Xin, M. Saqlain, F. Smarandache, and M. I. Ahamad, “An integrated model of neutrosophic TOPSIS with application in multi-criteria decision-making problem,” Neutrosophic Sets Syst., vol. 40, pp. 253–269, 2021.
[12] T. Gaber et al., “Thermogram breast cancer prediction approach based on neutrosophic sets and fuzzy c-means algorithm,” in 2015 37th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Ieee, 2015, pp. 4254–4257.
[13] G. I. Sayed and A. E. Hassanien, “Moth-flame swarm optimization with neutrosophic sets for automatic mitosis detection in breast cancer histology images,” Appl. Intell., vol. 47, pp. 397–408, 2017.
[14] K. M. Amin, A. I. Shahin, and Y. Guo, “A novel breast tumor classification algorithm using neutrosophic score features,” Measurement, vol. 81, pp. 210–220, 2016.
[15] M. Lotfollahi, M. Gity, J. Y. Ye, and A. Mahlooji Far, “Segmentation of breast ultrasound images based on active contours using neutrosophic theory,” J. Med. Ultrason., vol. 45, pp. 205–212, 2018.
[16] Nada A. Nabeeh , Alshaimaa A. Tantawy, A Neutrosophic Model for Blockchain Platform Selection based on SWARA and WSM, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 29-43 (Doi : https://doi.org/10.54216/NIF.010204)
[17] Mona Mohamed , Nissreen El Saber, Prioritization Thermochemical Materials based on Neutrosophic sets Hybrid MULTIMOORA Ranker Method, Neutrosophic and Information Fusion, Vol. 2 , No. 1 , (2023) : 08-22 (Doi : https://doi.org/10.54216/NIF.020101)
[18] Abedallah abualkishik , Rasha Almajed , Watson Thompson, Improving the perfoamnce of Fog-assisted Internet of Things Networks using Bipolar Trapezoidal Neutrosophic sets, International Journal of Wireless and Ad Hoc Communication, Vol. 6 , No. 1 , (2023) : 30-37 (Doi : https://doi.org/10.54216/IJWAC.060103)
[19] H. Sharma, A. Tandon, P. K. Kapur, and A. G. Aggarwal, “Ranking hotels using aspect ratings based sentiment classification and interval-valued neutrosophic TOPSIS,” Int. J. Syst. Assur. Eng. Manag., vol. 10, pp. 973–983, 2019.
[20] Ahmed Abdelhafeez , Hoda K Mohamed, Enhance the Performance of Bus Rapid Transit (BRT) through the Evaluation of Alternatives under an Integrated MCDM Neutrosophic Environment, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 08-15 (Doi : https://doi.org/10.54216/NIF.010201)
[21] Abdullah Ali Salamai, Evaluation and Selection of Cloud Service: A neutrosophic model, Neutrosophic and Information Fusion, Vol. 1 , No. 2 , (2023) : 16-25 (Doi : https://doi.org/10.54216/NIF.010202)
[22] Ahmed Abdelhafeez , Hoda K. Mohamed, Skin Cancer Detection using Neutrosophic c-means and Fuzzy c-means Clustering Algorithms, Journal of Intelligent Systems and Internet of Things, Vol. 8 , No. 1 , (2023) : 33-42 (Doi : https://doi.org/10.54216/JISIoT.080103)
[23] Gopal Chaudhary , Manju Khari , Amena Mahmoud, Intelligent Video Moving Target Detection Based on Multi-Attribute Single Value Medium Neutrosophic Method, Journal of Intelligent Systems and Internet of Things, Vol. 5 , No. 1 , (2021) : 49-59 (Doi : https://doi.org/10.54216/JISIoT.050105)
[24] S. Chen, G. I. Webb, L. Liu, and X. Ma, “A novel selective naïve Bayes algorithm,” Knowledge-Based Syst., vol. 192, p. 105361, 2020.
[25] M. El Kourdi, A. Bensaid, and T. Rachidi, “Automatic Arabic document categorization based on the Naïve Bayes algorithm,” in proceedings of the Workshop on Computational Approaches to Arabic Script-based Languages, 2004, pp. 51–58.
[26] R. Bhardwaj and S. Vatta, “Implementation of ID3 algorithm,” Int. J. Adv. Res. Comput. Sci. Softw. Eng., vol. 3, no. 6, 2013.
[27] M. Slocum, “Decision making using id3 algorithm,” Insight River Acad. J, vol. 8, no. 2, 2012.
[28] S. Zhang, D. Cheng, Z. Deng, M. Zong, and X. Deng, “A novel kNN algorithm with data-driven k parameter computation,” Pattern Recognit. Lett., vol. 109, pp. 44–54, 2018.
[29] A. Moldagulova and R. B. Sulaiman, “Using KNN algorithm for classification of textual documents,” in 2017 8th international conference on information technology (ICIT), IEEE, 2017, pp. 665–671.
[30] Gómez, Gustavo Adolfo Álvarez, Maikel Yelandi Leyva Vázquez, Jesús Estupiñán Ricardo. "Application of Neutrosophy to the Analysis of Open Government, its Implementation and Contribution to the Ecuadorian Judicial System." Neutrosophic Sets and Systems vol 52, pp.215-224., 2022.
