Using Logistic Regression Approach to Predicating Breast Cancer DATASET-Reference-Cited by-同舟云学术

Using Logistic Regression Approach to Predicating Breast Cancer DATASET

Published:2023 Issue: Volume: Page:581-591
ISSN:1860-949X
Container-title:The Effect of Information Technology on Business and Marketing Intelligence Systems
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Author:

Haziemeh Feras A.,Darawsheh Saddam Rateb^ORCID,Alshurideh Muhammad^ORCID,Al-Shaar Anwar Saud

Publisher

Springer International Publishing

Link

https://link.springer.com/content/pdf/10.1007/978-3-031-12382-5_31

Reference17 articles.

1. Agresti, A. (2018). An introduction to categorical data analysis. John Wiley & Sons.

2. Carpio, C. E., Sydorovych, O., & Marra, M. C. (2007). Relative importance of environmental attributes using logistic regression.

3. Chaurasia, V., & Pal, S. (2014). Data mining techniques: To predict and resolve breast cancer survivability. International Journal of Computer Science Mobile Computing IJCSMC, 3(1), 10–22.

4. Choi, T., Schervish, M. J., Schmitt, K. A., & Small, M. J. (2008). A Bayesian approach to a logistic regression model with incomplete information. Biometrics, 64(2), 424–430.

5. Chaurasia, V., & Pal, S. (2017). A novel approach for breast cancer detection using data mining techniques. International Journal of Innovative Research in Computer and Communication Engineering (An ISO 3297 2007 Certified Organisation), 2(1), 2456–2465.

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