More Web Proxy on the site http://driver.im/

research-article

Machine Learning Techniques for Breast Cancer Prediction

Authors:

Vishal FegadeAuthors Info & Claims

Volume 218, Issue C

Pages 1314 - 1320

https://doi.org/10.1016/j.procs.2023.01.110

Published: 01 January 2023 Publication History

Abstract

Breast cancer is main reason for mortality in woman. Prediction of breast cancer is a challenging task in medical data analysis. Doctors and pathologist required some automated tools to take decision and to differentiate between malignant and benign tumour. A machine learning (ML) algorithm helps lot to take decisions and to perform diagnosis from the data collected by medical field. Various researches show that ML techniques are helpful for decision making in breast cancer prediction. In this paper, we used various ML Classification techniques: Naïve Bayes(NB), Logistic regression (LR),Support vector machine(SVM),K-Nearest Neighbor (KNN), Decision Tree(DT), and ensemble techniques: Random forest(RF), Adaboost, XGBoost on breast cancer dataset and evaluated by using different performance measure. It has been found that both decision tree and XGBoost classifier has highest accuracy 97% among all model and highest AUC 0.999 obtained for XGBoost classifier.

References

[1]

H. Sung, J. Ferlay, R.L. Siegel, M. Laversanne, I. Soerjomataram, A. Jemal, F. Bray, Global cancer statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries, CA: a cancer journal for clinicians 71 (3) (2021) 209–249.

[2]

J. Ferlay, M. Colombet, I. Soerjomataram, D.M. Parkin, M. Piñeros, A. Znaor, F. Bray, Cancer statistics for the year 2020: An overview, International Journal of Cancer 149 (4) (2021) 778–789.

[3]

S Patil, D Kirange, V. Nemade, Predictive modelling of brain tumor detection using deep learning, Journal of Critical Reviews 7 (2020).

[4]

V. Nemade, S. Pathak, A.K. Dubey, D. Barhate, A Review and Computational Analysis of Breast Cancer Using Different Machine Learning Techniques 12 (3) (2022) 111–118.

[5]

P Chaturvedi, A Jhamb, M Vanani, V. Nemade, Prediction and classification of lung cancer using machine learning techniques, InIOP Conference Series: Materials Science and Engineering 1099 (1) (2021) Mar 1IOP Publishing.

[6]

D. Abdelhafiz, C. Yang, R. Ammar, S. Nabavi, Deep convolutional neural networks for mammography: advances, challenges and applications, BMC bioinformatics 20 (11) (2019) 1–20.

[7]

V. Nemade, S. Pathak, A.K. Dubey, A Systematic Literature Review of Breast Cancer Diagnosis Using Machine Intelligence Techniques, Archives of Computational Methods in Engineering (2022) 1–30.

[8]

L Dora, S Agrawal, R Panda, A Abraham, Optimal breast cancer classification using Gauss–Newton representation based algorithm, Expert Systems with Applications 85 (2017) 134–145.

Digital Library

[9]

OI Obaid, MA Mohammed, MK Ghani, A Mostafa, F. Taha, Evaluating the performance of machine learning techniques in the classification of Wisconsin Breast Cancer, International Journal of Engineering & Technology 7 (2018) 160–166. 4.36.

[10]

P. Yellamma, C.S. Chowdary, G. Karunakar, B.S. Rao, V. Ganesan, Breast Cancer Diagnosis Using MLP Back Propagation, International Journal 8 (9) (2020).

[11]

M.A. Naji, S. El Filali, K. Aarika, E.H. Benlahmar, R.A. Abdelouhahid, O. Debauche, Machine Learning Algorithms For Breast Cancer Prediction And Diagnosis, Procedia Computer Science 191 (2021) 487–492.

Digital Library

[12]

S. Kabiraj, M. Raihan, N. Alvi, M. Afrin, L. Akter, S.A. Sohagi, E. Podder, Breast cancer risk prediction using XGBoost and random forest algorithm, in: 2020 11th international conference on computing, communication and networking technologies (ICCCNT), IEEE, 2020, pp. 1–4.

[13]

P. Liu, B. Fu, S.X. Yang, L. Deng, X. Zhong, H. Zheng, Optimizing survival analysis of XGBoost for ties to predict disease progression of breast cancer, IEEE Transactions on Biomedical Engineering 68 (1) (2020) 148–160.

[14]

S. Nanglia, M. Ahmad, F.A. Khan, N.Z. Jhanjhi, An enhanced Predictive heterogeneous ensemble model for breast cancer prediction, Biomedical Signal Processing and Control 72 (2022).

[15]

M. Islam, M. Haque, H. Iqbal, M. Hasan, M. Hasan, M.N. Kabir, Breast cancer prediction: a comparative study using machine learning techniques, SN Computer Science 1 (5) (2020) 1–14.

[16]

M. Amrane, S. Oukid, I. Gagaoua, T. Ensari, Breast cancer classification using machine learning, in: 2018 electric electronics, computer science, biomedical engineerings' meeting (EBBT), IEEE, 2018, pp. 1–4.

[17]

H. Dhahri, E. Al Maghayreh, A. Mahmood, W. Elkilani, M. Faisal Nagi, Automated breast cancer diagnosis based on machine learning algorithms, Journal of healthcare engineering (2019).

[18]

S Kharya, S. Soni, Weighted naive bayes classifier: a predictive model for breast cancer detection, International Journal of Computer Applications 133 (9) (2016) 32–37. Jan.

[19]

Q Huang, Y Chen, L Liu, D Tao, X. Li, On combining biclustering mining and AdaBoost for breast tumor classification, IEEE Transactions on Knowledge and Data Engineering 32 (4) (2019) 728–738. Jan 14.

[20]

S Ara, A Das, A. Dey, Malignant and benign breast cancer classification using machine learning algorithms, in: In2021 International Conference on Artificial Intelligence (ICAI), IEEE, 2021, pp. 97–101. Apr 5.

[21]

MA. Jabbar, Breast cancer data classification using ensemble machine learning, Engineering and Applied Science Research 48 (1) (2021) 65–72. Jan 27.

[22]

TR Mahesh, V Vinoth Kumar, V Vivek, KM Karthick Raghunath, G Sindhu Madhuri, Early predictive model for breast cancer classification using blended ensemble learning, International Journal of System Assurance Engineering and Management (2022) 1–10. Jun 26.

[23]

https://archive.ics.uci.edu/ml/datasets/breast±cancer±wisconsin±(diagnostic)

[24]

V Chaurasia, S Pal, BB. Tiwari, Prediction of benign and malignant breast cancer using data mining techniques, Journal of Algorithms & Computational Technology 12 (2) (2018) 119–126. Jun.

Cited By

Shankari RLeena Jasmine JMary Joans S(2024)Digital mammogram based robust feature extraction and selection for effective breast cancer classification in earlier stageJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23511646:2(4593-4607)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-235116
Gholap AUddin MFaiyazuddin MOmri AGowri SKhalid M(2024)Advances in artificial intelligence for drug delivery and developmentComputers in Biology and Medicine10.1016/j.compbiomed.2024.108702178:COnline publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108702

Index Terms

Machine Learning Techniques for Breast Cancer Prediction
1. Applied computing
  1. Life and medical sciences
2. Computing methodologies
  1. Machine learning

Index terms have been assigned to the content through auto-classification.

Recommendations

BCPUML: Breast Cancer Prediction Using Machine Learning Approach—A Performance Analysis
Abstract
Breast cancer is taking the lives of women globally. It’s one of the most common malignancies in women, as well as the leading cause of cancer-related death. Even though there are no therapies for breast cancer, early detection and diagnosis are ...
Prediction of Breast Cancer using Machine Learning Techniques
IC3-2022: Proceedings of the 2022 Fourteenth International Conference on Contemporary Computing

Breast cancer is a topic that is frequently discussed these days. It is one of the most widespread diseases and forms of cancer. The National Cancer Institute says that the second most frequent malignancy in women is breast cancer. Every year, around ...
Machine learning models in breast cancer survival prediction
BACKGROUND:
Breast cancer is one of the most common cancers with a high mortality rate among women. With the early diagnosis of breast cancer survival will increase from 56% to more than 86%. Therefore, an accurate and reliable system is necessary for the ...

Comments

Please enable JavaScript to view thecomments powered by Disqus.

Information & Contributors

Information

Published In

cover image Procedia Computer Science

Procedia Computer Science Volume 218, Issue C

2023

2869 pages

ISSN:1877-0509

EISSN:1877-0509

Issue’s Table of Contents

Copyright © 2023.

Publisher

Elsevier Science Publishers B. V.

Netherlands

Publication History

Published: 01 January 2023

Author Tag

Breast Cancer;Ensemble Techniques;Machine Learning

Qualifiers

Research-article

Contributors

Other Metrics

View Article Metrics

Bibliometrics & Citations

Bibliometrics

Article Metrics

2
Total Citations
View Citations
0
Total Downloads

Downloads (Last 12 months)0
Downloads (Last 6 weeks)0

Reflects downloads up to 07 Jan 2025

Other Metrics

View Author Metrics

Citations

Cited By

Shankari RLeena Jasmine JMary Joans S(2024)Digital mammogram based robust feature extraction and selection for effective breast cancer classification in earlier stageJournal of Intelligent & Fuzzy Systems: Applications in Engineering and Technology10.3233/JIFS-23511646:2(4593-4607)Online publication date: 14-Feb-2024
https://dl.acm.org/doi/10.3233/JIFS-235116
Gholap AUddin MFaiyazuddin MOmri AGowri SKhalid M(2024)Advances in artificial intelligence for drug delivery and developmentComputers in Biology and Medicine10.1016/j.compbiomed.2024.108702178:COnline publication date: 19-Sep-2024
https://dl.acm.org/doi/10.1016/j.compbiomed.2024.108702

View Options

View options

Media

Figures

Other

Tables

View Issue’s Table of Contents