International Journal of Information Engineering and Electronic Business (IJIEEB)
IJIEEB Vol. 10, No. 5, 8 Sep. 2018
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A Study on Test Variable Selection and Balanced Data for Cervical Cancer Disease
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Author(s)
Kemal Akyol
1,*
Index Terms
Cervical cancer, the importance of test variables, random over-sampling, random under-fitting, stability selection, random forest
Abstract
Cancer is a pestilent disease. One of the most important cancer kinds, cervical cancer is a malignant tumor which threats women's life. In this study, the importance of test variables for cervical cancer disease is investigated by utilizing Stability Selection method. Also, Random Under-Sampling and Random Over-Sampling methods are implemented on the dataset. In this context, the learning model is designed by using Random Forest algorithm. The experimental results show that Stability Selection, Random Over-Sampling and Random Forest based model are more successful, approximately 98% accuracy.
Cite This Paper
Kemal Akyol, "A Study on Test Variable Selection and Balanced Data for Cervical Cancer Disease", International Journal of Information Engineering and Electronic Business(IJIEEB), Vol.10, No.5, pp. 1-7, 2018. DOI:10.5815/ijieeb.2018.05.01
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