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Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection

Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection

Sanjay Kumar Singh, Anjali Goyal
ISBN13: 9781668471364|ISBN10: 1668471361|EISBN13: 9781668471371
DOI: 10.4018/978-1-6684-7136-4.ch019
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MLA

Singh, Sanjay Kumar, and Anjali Goyal. "Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection." Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, IGI Global, 2023, pp. 347-370. https://doi.org/10.4018/978-1-6684-7136-4.ch019

APA

Singh, S. K. & Goyal, A. (2023). Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection. In I. Management Association (Ed.), Research Anthology on Medical Informatics in Breast and Cervical Cancer (pp. 347-370). IGI Global. https://doi.org/10.4018/978-1-6684-7136-4.ch019

Chicago

Singh, Sanjay Kumar, and Anjali Goyal. "Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection." In Research Anthology on Medical Informatics in Breast and Cervical Cancer, edited by Information Resources Management Association, 347-370. Hershey, PA: IGI Global, 2023. https://doi.org/10.4018/978-1-6684-7136-4.ch019

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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.