Abstract
Breast Cancer is one of the topmost well-known diseases with a high death rate among women. It is a non-communicable disease that is seen in numerous women in all over the world. With the early analysis of this disease, the endurance will arise from 56% to over 86%. In this analysis, several unsupervised learning techniques were used with the kernel techniques of Principle Component Analysis (PCA). K-Means and several Hierarchical Clustering techniques with different linkages such as ward, complete, and average were applied and highest accuracy of 70.91% was obtained from Hierarchical Clustering with average linkage. The better performances were in Recall and F1-score from K-Means compared to Ward and Complete linkage clustering techniques. The Specificity, Precision, Recall, and F1-score have shown satisfactory performances in Average linkage with the values of 60%, 70.58%, 80%, and 75% correspondingly.
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References
B. Santhosh Kumar, T. Daniya, J. Ajayan, Breast cancer prediction using machine learning algorithms. Int. J. Adv. Sci. Technol. 29(03), 7819–7828 (2020)
J. Joshi, R. Doshi, J. Patel, Diagnosis of breast cancer using clustering data mining approach. Int. J. Comput. Appl. 101(10), 13–17 (2014). https://doi.org/10.5120/17722-7611
E. Bahmani, M. Jamshidi, A.A. Shaltooki, Breast cancer prediction using a hybrid data mining model. Int. J. Inform. Vis. 3(4) (2019). https://doi.org/10.30630/joiv.3.4.240
T.F. Hack, L.F. Degner, Coping with breast cancer: a cluster analytic approach. Breast Cancer Res. Treat. 54(3), 185–194 (1999). https://doi.org/10.1023/a:1006145504850
C.H. Chen, A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection. Appl. Soft Comput. 20, 4–14 (2014). https://doi.org/10.1016/j.asoc.2013.10.024
Z. Rustam, S. Hartini, Classification of breast cancer using fast fuzzy clustering based on kernel, in IOP Conference Series: Materials Science and Engineering, vol. 546, p. 052067 (2019). https://doi.org/10.1088/1757-899x/546/5/052067
S. Kabiraj, M. Raihan, N. Alvi, M. Afrin, L. Akter, S.A. Sohagi, E. Podder, Breast cancer risk prediction using XGBoost and random forest algorithm, in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (Kharagpur, India, 2020), pp. 1–4. https://doi.org/10.1109/ICCCNT49239.2020.9225451
S. Kabiraj, L. Akter, M. Raihan, N. J. Diba, E. Podder, M.M. Hassan, Prediction of recurrence and non-recurrence events of breast cancer using bagging algorithm, in 2020 11th International Conference on Computing, Communication and Networking Technologies (ICCCNT) (Kharagpur, India, 2020), pp. 1–5. https://doi.org/10.1109/ICCCNT49239.2020.9225440
N.K. Haneefa, B.M.A. Desai, R. Sarathi, M. Rathinam, Dendrogram based clustering and separation of individual and simultaneously active incipient discharges in transformer insulation, in 2020 International Conference on Signal Processing and Communications (SPCOM) (Bangalore, India, 2020), pp. 1–5. https://doi.org/10.1109/SPCOM50965.2020.9179572
J. Wang, Q. Guo, Kernel principal component analysis: radial basis function neural networks based soft-sensor modeling of polymerizing process optimized by cultural differential evolution algorithm. Instrum. Sci. Technol. 41(1), 18–36 (2013). https://doi.org/10.1080/10739149.2012.710884
E. Gose, R. Johnsonbaugh, S. Jost, Pattern Recognition and Image Analysis (Prentice Hall PTR, Upper Saddle River, 1996)
J. Han, M. Kamber, J. Pei, Data Mining (Elsevier/Morgan Kaufmann, Amsterdam, 2012)
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Akter, L., Raihan, M., Raihan, M.M.S., Ghosh, M., Alvi, N., Ferdib-Al-Islam (2022). Breast Cancer Risk Prediction Using Different Clustering Techniques. In: Khanna, A., Gupta, D., Bhattacharyya, S., Hassanien, A.E., Anand, S., Jaiswal, A. (eds) International Conference on Innovative Computing and Communications. Advances in Intelligent Systems and Computing, vol 1388. Springer, Singapore. https://doi.org/10.1007/978-981-16-2597-8_16
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DOI: https://doi.org/10.1007/978-981-16-2597-8_16
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