Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)

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Comparative Analysis of Machine Learning Algorithms for Cervical Cancer Prediction

Authors
Ireddi Rakshitha1, Thokala Vasisri1, Mayaluri Anusha1, M. Sucharitha1, *
1VIT-AP University, Amaravati, Andhra Pradesh, India
*Corresponding author. Email: sucharitha.jackson@vitap.ac.in
Corresponding Author
M. Sucharitha
Available Online 21 December 2023.
DOI
10.2991/978-94-6463-314-6_16How to use a DOI?
Keywords
Performance; Algorithms; Predictive Modeling
Abstract

Cervical cancer constitutes a significant public health concern and early diagnosis plays an important role in the patient’s recovery. In this study, we investigated the utilization of various algorithms in machine learning to predict cancer with best accuracy. The objective of the paper is to identify the most reliable predictors of cervical cancer through comparative analysis. To achieve this goal, we obtained information including medical and demographic characteristics of different patients. The data has been prepared for analysis by addressing any missing values, normalizing features, and by resolving intra-class imbalance. We used algorithms like Support Vector Machine (SVM), K-Nearest Neighbors (K-NN), and Decision Tree, Random Forest, XG Boost etc. Metrics like precision, accuracy, and recall, and area under receiver operating characteristic curve (AUC-ROC) are used for evaluating accuracy and discrimination. Performance of these models is also compared to real- world applications. We highlight significance of machine learning algorithms in early prediction of cervical cancer. Among all the models used, XG Boost is getting higher accuracy of 99.22%. These findings provide valuable insights to researchers, physicians and policy makers, leading to ways to enhance care for patient and to mitigate the global impact of cervical cancer worldwide.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
Series
Atlantis Highlights in Computer Sciences
Publication Date
21 December 2023
ISBN
10.2991/978-94-6463-314-6_16
ISSN
2589-4900
DOI
10.2991/978-94-6463-314-6_16How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Ireddi Rakshitha
AU  - Thokala Vasisri
AU  - Mayaluri Anusha
AU  - M. Sucharitha
PY  - 2023
DA  - 2023/12/21
TI  - Comparative Analysis of Machine Learning Algorithms for Cervical Cancer Prediction
BT  - Proceedings of the International e-Conference on Advances in Computer Engineering and Communication Systems (ICACECS 2023)
PB  - Atlantis Press
SP  - 161
EP  - 169
SN  - 2589-4900
UR  - https://doi.org/10.2991/978-94-6463-314-6_16
DO  - 10.2991/978-94-6463-314-6_16
ID  - Rakshitha2023
ER  -