Breast Cancer Prediction Using K-mean Classification Algorithm with Self-adaptive Weight

Main Article Content

Arika Thammano
Muthita Wangkid
Arit Thammano

Abstract

“Breast cancer” is the first-rank non-communicable disease found in women both in Thailand and the world at large. The statistic of the National Cancer Institute reveals that the number of Thai women suffering from breast cancer is likely to increase every year. If breast cancer can be found at its early stage and cured properly, the risk of mortality can be considerably reduced. This research presents K-mean classification algorithm with self-adaptive weight and the program for breast cancer prediction using Python programming language with an aim to help identify and cure the early-stage breast cancer patients in a timely manner. The algorithm presented in this research has been modified from K-mean clustering algorithm to be able to perform the classification task and to have the ability to self-adapt the weights of the features in the Euclidean distance equation. The efficiency of algorithm and breast cancer prediction program was tested using Breast Cancer Coimbra data set. The result shows that the breast cancer prediction using the proposed algorithm is more accurate than other artificial intelligence algorithms.

Downloads

Download data is not yet available.

Article Details

How to Cite
[1]
A. Thammano, M. Wangkid, and A. Thammano, “Breast Cancer Prediction Using K-mean Classification Algorithm with Self-adaptive Weight”, JIST, vol. 10, no. 2, pp. 1-9, Aug. 2020.
Section
Research Article: Soft Computing (Detail in Scope of Journal)
Author Biography

Arit Thammano, King Mongkut’s Institute of Technology Ladkrabang

กรรมการ/คณะเทคโนโลยีสารสนเทศ

References

[1] National Cancer Institute, Department of Medical Services, Ministry of Public Health, Thailand, Hospital-based cancer registry. Bangkok: National Cancer Institute, 2013.

[2] National Cancer Institute, Department of Medical Services, Ministry of Public Health, Thailand, “National Cancer Institute Strategic Plan, 2019 to 2022,” Bangkok: National Cancer Institute, 2020.

[3] A. Ratanawichitrasin, “What is the difference between breast ultrasound and mammography,” Siriraj E-public Library, July, 2020. [Online]. Available: https://www.si.mahidol.ac.th/sidoctor/epl/articledetail.asp?id=307. [Accessed July. 10, 2020].

[4] Khonkaen Ram Hospital, “Breast cancer screening using digital mammography,” Khonkaen Ram Hospital, July, 2020. [Online]. Available: http://www.khonkaenram.com /th/services/health-information/healtharticles/ mammogram. [Accessed July. 10, 2020].

[5] R. Siegel, J. Ma, Z. Zou, and A. Jemal, “Cancer statistics,” CA Cancer J Clin, Vol. 64, No. 1, pp. 9-29, 2014.

[6] Siriraj Piyamaharajkarun Hospital, “Breast cancer treatment,” Siriraj Piyamaharajkarun Hospital, July, 2020. [Online]. Available: https://www.siphhospital .com /th/news/article/share/1002/Breast-Cancer-Treatment. [Accessed July. 10, 2020].

[7] National Cancer Institute, Department of Medical Services, Ministry of Public Health, Thailand, Hospital-based cancer registry. Bangkok: National Cancer Institute, 2010.

[8] S. Senawong, “Types of tumor markers,” Siriraj E-public Library, July, 2020. [Online]. Available: https://www.si.mahidol.ac.th/sidoctor/e-pl/articledetail .asp?id=618. [Accessed July. 10, 2020].

[9] S. Senawong, “Can a blood test detect cancer?,” Siriraj E-public Library, [Online]. Available: https://www.si.mahidol.ac.th/sidoctor/e-pl/articledetail .asp?id=619. [Accessed July. 10, 2020].

[10] F. Sardouk, A. D. Duru, and O. Bayat, “Classification of breast cancer using data mining,” American Scientific Research Journal for Engineering, Technology, and Sciences, Vol. 51, No. 1, pp. 38-46, 2019.

[11] R. Ray, A. A. Abdullah, D. K. Mallick, and S. R. Dash, “Classification of benign and malignant breast cancer using supervised machine learning algorithms based on image and numeric datasets,” Journal of Physics: Conference Series, Vol. 1372, 2019, 012062, DOI:10.1088/1742-6596/1372/1/012062, 2019.

[12] Y. D. Austria, J. P. Lalata, L. B. Sta. Maria, Jr., J. E. E. Goh, M. L. I. Goh, H. N.Vicente, “Comparison of machine learning algorithms in breast cancer prediction using the coimbra dataset,” International Journal of Simulation: Systems, DOI: 10.5013/IJSSST.a.20.S2.23, 2019.

[13] M. Patrício, J. Pereira, J. Crisóstomo, P. Matafome, M. Gomes, R. Seiça, and F. Caramelo, “Using resistin, glucose, age and BMI to predict the presence of breast cancer,” BMC Cancer, Vol. 18, No. 1, 2018.

[14] D. Dua and C. Graff, UCI Machine Learning Repository [http://archive.ics.uci.edu/ml]. Irvine, CA: University of California, School of Information and Computer Science, 2019.