Classification of Cervical Cancer Dataset

TL;DR

This study evaluates various classification techniques and feature selection methods on a cervical cancer dataset, demonstrating high accuracy and identifying key predictive features using decision trees and data balancing strategies.

Contribution

The paper compares feature selection and classification methods for cervical cancer prediction, highlighting the effectiveness of decision trees and data balancing techniques.

Findings

Achieved 97.5% accuracy in classification.

Identified key predictive features such as age and sexual history.

Decision Tree classifier outperformed other models.

Abstract

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.