Evaluation of Recursive Feature Elimination and LASSO Regularization-based optimized feature selection approaches for cervical cancer prediction

Abstract

Cervical cancer is one of the leading causes of premature mortality among women worldwide and more than 85% of these deaths are in developing countries. There are several risk factors associated with cervical cancer. In this research, the aim is to develop a predictive model for predicting the outcome of patient's cervical cancer results, given risk patterns from individual medical records and preliminary screening. This work presents a machine learning method using Decision Tree (DT) algorithm to analyze the risk factors of cervical cancer. Recursive Feature Elimination (RFE) and least absolute shrinkage and selection operator (LASSO) feature selection techniques were fully explored to determine the most important attributes for cervical cancer prediction. Comparative analysis of the 2 feature selection techniques were performed to show the importance of feature selection in cervical cancer prediction. Based on the result of the analysis, we can conclude that the proposed model produced the highest accuracy of 98% and 96% respectively while using DT with RFE and LASSO feature selection techniques respectively.