  Cervical cancer is the leading gynecological malignancy worldwide. This paper
presents diverse classification techniques and shows the advantage of feature
selection approaches to the best predicting of cervical cancer disease. There
are thirty-two attributes with eight hundred and fifty-eight samples. Besides,
this data suffers from missing values and imbalance data. Therefore,
over-sampling, under-sampling and embedded over and under sampling have been
used. Furthermore, dimensionality reduction techniques are required for
improving the accuracy of the classifier. Therefore, feature selection methods
have been studied as they divided into two distinct categories, filters and
wrappers. The results show that age, first sexual intercourse, number of
pregnancies, smokes, hormonal contraceptives, and STDs: genital herpes are the
main predictive features with high accuracy with 97.5%. Decision Tree
classifier is shown to be advantageous in handling classification assignment
with excellent performance.
