Scopus Indexed Paper

Paper Details


Title
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.
Keywords
Ensemble method, Bagging, Machine learning, cervical cancer, risk factors
Authors
Enamul Karim, Nafis Neehal
Phone
Journal or Conference Name
1st International Conference on Robotics, Electrical and Signal Processing Techniques, ICREST 2019
Publish Year
2019
Indexing
Scopus