Performance analysis of machine learning algorithms for cervical cancer detection

Abstract

Cervical cancer is second most prevailing cancer in women all over the world and the Pap smear is one of the most popular techniques used to diagnosis cervical cancer at an early stage. Developing countries like India has to face the challenges in order to handle more cases day by day. In this article, various online and offline machine learning algorithms has been applied on benchmarked data sets to detect cervical cancer. This article also addresses the problem of segmentation with hybrid techniques and optimizes the number of features using extra tree classifiers. Accuracy, precision score, recall score, and F1 score are increasing in the proportion of data for training and attained up to 100% by some algorithms. Algorithm like logistic regression with L1 regularization has an accuracy of 100%, but it is too much costly in terms of CPU time in comparison to some of the algorithms which obtain 99% accuracy with less CPU time. The key finding in this article is the selection of the best machine learning algorithm with the highest accuracy. Cost effectiveness in terms of CPU time is also analysed. This chapter published as an Open Access Chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/4.0/) which permits unrestricted use, distribution, and production in any medium, provided the author of the original work and original publication source are properly credited.