An Empirical Study of Cervical Cancer Diagnosis using Ensemble Methods

Abstract

Cervical Cancer, being one of the most pressing issues now-a-days, needs to be addressed properly. With a view to achieving an accurate diagnosis method for Cervical Cancer by screening the risk factors, different machine learning approaches have been taken over time. But by analyzing the performances of most of state-of-the-art approaches, it was inferred that there is still room for improvement by developing a more accurate model. Hence, in this paper an approach using ensemble methods with SVM as the base classifier has been taken. The ensemble method with Bagging technique achieved an accuracy of 98.12% with very high precision, recall and f-measure value.