DeepAI AI Chat
Log In Sign Up

Classification of Cervical Cancer Dataset

by   Avishek Choudhury, et al.

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 classifier is shown to be advantageous in handling classification assignment with excellent performance.


page 1

page 2

page 3

page 4


Handcrafted Feature Selection Techniques for Pattern Recognition: A Survey

The accuracy of a classifier, when performing Pattern recognition, is mo...

A Study of Feature Selection and Extraction Algorithms for Cancer Subtype Prediction

In this work, we study and analyze different feature selection algorithm...

Feature selection in functional data classification with recursive maxima hunting

Dimensionality reduction is one of the key issues in the design of effec...

DimReduction - Interactive Graphic Environment for Dimensionality Reduction

Feature selection is a pattern recognition approach to choose important ...

Machine Learning Methods in the Computational Biology of Cancer

The objectives of this "perspective" paper are to review some recent adv...