Open AccessA Study on Test Variable Selection and Balanced Data for Cervical Cancer Disease
Author(s) -
Kemal Akyol
Publication year - 2018
Publication title -
international journal of information engineering and electronic business
Language(s) - English
Resource type - Journals
eISSN - 2074-9031
pISSN - 2074-9023
DOI - 10.5815/ijieeb.2018.05.01
Subject(s) - random forest , cervical cancer , sampling (signal processing) , context (archaeology) , selection (genetic algorithm) , stability (learning theory) , computer science , statistics , cancer , disease , medicine , artificial intelligence , machine learning , mathematics , biology , computer vision , paleontology , filter (signal processing)
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.
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