Computational Prediction of Cervical Cancer Diagnosis Using Ensemble-Based Classification Algorithm

Abstract

Abstract Cervical cancer is one of the most common cancers among women in the world. As at the earlier stage, cervical cancer has fewer symptoms. Cancer research is vital as the prognosis of cancer enables clinical applications for patients. In this study, we demonstrate a new approach that applies an ensemble approach to machine learning models for the automatic diagnosis of cervical cancer. The dataset used in the study is the cervical cancer dataset available at the University of California Irvine database repository. Initially, missing values are imputed (k-nearest neighbors) and then the data are balanced (oversampled). Two feature selection approaches are used to extract the most significant features. The proposed stacking architecture, applied for the first time on the cervical cancer dataset, used time elapse of 5.6 s and achieved an area under the curve score of 99.7% performing better than the methods used in previous works. The objective of the study is to propose a computational model that can predict the diagnosis of cervical cancer efficiently. Further, the proposed learning architecture is gauged with several ensemble approaches like random forest, gradient boosting, voting ensemble and weighted voting ensemble to perceive the enhancement.