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Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection

Author

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  • Sanjay Kumar Singh

    (I.K. Gujral Punjab Technical University, Kapurthala, Punjab, India)

  • Anjali Goyal

    (Department of Computer Applications, GNIMT, Ludhiana, Punjab, India)

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.

Suggested Citation

  • Sanjay Kumar Singh & Anjali Goyal, 2020. "Performance Analysis of Machine Learning Algorithms for Cervical Cancer Detection," International Journal of Healthcare Information Systems and Informatics (IJHISI), IGI Global, vol. 15(2), pages 1-21, April.
    • Handle: RePEc:igg:jhisi0:v:15:y:2020:i:2:p:1-21
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    Cited by:

    1. Huseyin Cagan Kilinc & Iman Ahmadianfar & Vahdettin Demir & Salim Heddam & Ahmed M. Al-Areeq & Sani I. Abba & Mou Leong Tan & Bijay Halder & Haydar Abdulameer Marhoon & Zaher Mundher Yaseen, 2023. "Daily Scale River Flow Forecasting Using Hybrid Gradient Boosting Model with Genetic Algorithm Optimization," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 37(9), pages 3699-3714, July.

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