Open Access
| Issue |
BIO Web Conf.
Volume 117, 2024
International Conference on Life Sciences and Technology (ICoLiST 2023)
|
|
|---|---|---|
| Article Number | 01029 | |
| Number of page(s) | 12 | |
| DOI | https://doi.org/10.1051/bioconf/202411701029 | |
| Published online | 05 July 2024 | |
- Pourzand, A., Fakhree, M. B., Hashemzadeh, S., Halimi, M., & Daryani, A. (2011). Hormone Receptor Status in Breast Cancer and its Relation to Age and Other Prognostic Factors. Breast Cancer: Basic and Clinical Research, 87-92. [Google Scholar]
- Verkooijen, H. M., Chappuis, P. O., Rapiti, E., Vlastos, G., Fioretta, G., Sarp, S., Sappino, A.P., Schubert, H., and Bouchardy, C. (2006). Impact of Familial Risk Factors on Management and Survival of Early-Onset Breast Cancer: a Population-Based Study. British Journal of Cancer, 231-238. [CrossRef] [PubMed] [Google Scholar]
- Houssami, N., Ciatto, S., Martinelli, F., Bonardi, R., & Duffy, S. W. (2009). Early Detection of Second Breast Cancers Improves Prognosis in Breast Cancer Survivors. Annals of Oncology, 1505-1510. [CrossRef] [PubMed] [Google Scholar]
- Muhartono, Ramanisa, S., Mutiara, H., & Riduan, R. J. (2016). Hubungan Antara Status Reseptor Estrogen, Reseptor Progesteron dan Human Epidermal Growth Factor Receptor 2 dengan Derajat Keganasan Karsinoma Payudara Invasif. Majalah Kedokteran Andalas, 65-72. [Google Scholar]
- Dunnwald, L.K., Rossing, M.A., and Li, C.I. (2007). Hormone Receptor Status, Tumor Characteristics, and Prognosis: a Prospective Cohort of Breast Cancer Patients. Breast Cancer Res. 9(1), 1-10. [CrossRef] [Google Scholar]
- Seshadri, R., Firgaira, F.A., Horsfall, D.J., McCaul, K., Setlur, V., and Kitchen, P. (1993). Clinical Significance of HER-2/Neu Oncogene Amplification in Primary Breast Cancer. The South Australian Breast Cancer Group. J. Clin. Oncol. 11(10), 1936-42. [Google Scholar]
- Kurniawan, M. F., & Ivandri. (2017). Komparasi Algoritma Data Mining untuk Klasifikasi Penyakit Kanker Payudara. IC-Tech, 1-8. [Google Scholar]
- Chawla, N.V., Lazarevic, A., Hall, L.O., and Bowyer, K.W. (2003). SMOTEBoost: Improving Prediction of The Minority Class in Boosting. European Conference on Principles of Data Mining and Knowledge Discovery, 107-119. [Google Scholar]
- Fausett, L. (1994). Fundamentals of Neural Netwroks Architectures, Algorithms, and Applications. London: Prentice Hall, Inc. [Google Scholar]
- Hamamoto, Y., Uchimura, S., Tomita, S. (1997). A Bootstrap Technique for Nearest Neighbor Clasifier Design. IEEE Transactions On Pattern Analysis And Machine Intelligence, vol. 19, no. 1, pp. 73-79. [Google Scholar]
- Alpaydin, E. (1997). Voting over multiple condensed nearest neighbors, Artificial Intelligence Review, 11, pp. 115-132. [CrossRef] [Google Scholar]
- Prasetyo, E. (2012). Data Mining Konsep dan Aplikasi Menggunakan MATLAB. Yogyakarta: Andi Yogyakarta. [Google Scholar]
- Lopes, N., & Ribeiro, B. (2015). On the Impact of Distance Metrics in Instance-Based Learning Algorithms. Iberian Conference on Pattern Recognition and Image Analysis (pp. 48-56). Springer. [Google Scholar]
- Bobrowski, L., & Topczewska, M. (2004). Improving the K-NN Classification with the Euclidean Distance Through Linear Data Transformations. Industrial Conference on Data Mining (pp. 23-32). Springer. [Google Scholar]
- Witten, I. H., Frank, E., & Hall, M. A. (2011). Data Mining Practical Machine Learning Tools and Techniques (3rd ed). USA: Elsevier. [Google Scholar]
- Erke, A. R., & Pattynama, P. M. (1998). Receiver Operating Characteristic (ROC) Analysis: Basic Principles and Aplications in Radiology. European Journal of Radiology, 88-94. [PubMed] [Google Scholar]
- Chou, S., Shan, J., Guo, Y., & Zhang, L. (2010). Automated Breast Cancer Detection and Classification Using Ultrasound Image: A Survey. Pattern Recognition, 299-317. [Google Scholar]
- Zweig, M. H., & Campbell, G. (1993). Receiver Operating Characteristic (ROC) Plots : A Fundamental Evaluation Clinical Medicine. Clinical Chemistry, 561-577. [CrossRef] [PubMed] [Google Scholar]
- Bekkar, M., Djemaa, H., & Alitouche, T. (2013). Evaluation Measures for Models Assesment Over mbalanced Data Sets. Journal of Information Engginering and Application, 3(10), 1-13. [Google Scholar]
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