Abstract
Nowadays, Breast cancer has risen to become one of the most prominent causes of death in recent years. Among all malignancies, this is the most frequent and the major cause of death for women globally. Manually diagnosing this disease requires a good amount of time and expertise. Breast cancer detection is time-consuming, and the spread of the disease can be reduced by developing machine-based breast cancer predictions. In Machine learning, the system can learn from prior instances and find hard-to-detect patterns from noisy or complicated data sets using various statistical, probabilistic, and optimization approaches. This work compares several machine learning algorithms' classification accuracy, precision, sensitivity, and specificity on a newly collected dataset. In this work Decision tree, Random Forest, Logistic Regression, Naïve Bayes, and XGBoost, these five machine learning approaches have been implemented to get the best performance on our dataset. This study focuses on finding the best algorithm that can forecast breast cancer with maximum accuracy in terms of its classes. This work evaluated the quality of each algorithm's data classification in terms of efficiency and effectiveness. And also compared with other published work on this domain. After implementing the model, this study achieved the best model accuracy, 94% on Random Forest and XGBoost.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
T.J. Key, P.K. Verkasalo, E. Banks, Epidemiology of breast cancer. Lancet Oncol. 2(3), 133–140 (2001)
U.S. Cancer Statistics Working Group (2012) United States Cancer Statistics: 1999–2008 Incidence and Mortality Web-based Report. Department of Health and Human Services, Centers for Disease Control and Prevention, and National Cancer Institute, Atlanta (GA)
V. Chaurasia, S. Pal, Data mining techniques: to predict and resolve breast cancer survivability. Int. J. Comput. Sci. Mob. Comput. IJCSMC 3(1), 10–22 (2014)
A. Djebbari, Z. Liu, S. Phan, F. Famili, An ensemble machine learning approach to predict survival in breast cancer. Int. J. Comput. Biol. Drug Des. 1(3), 275–294 (2008)
S. Aruna, S.P. Rajagopalan, L.V. Nandakishore, Knowledge based analysis of various statistical tools in detecting breast cancer. Comput. Sci. Inform. Technol. 2(2011), 37–45 (2011)
A.F.M. Agarap, On breast cancer detection: an application of machine learning algorithms on the Wisconsin diagnostic dataset, in Proceedings of the 2nd International Conference on Machine Learning and Soft Computing (2018, February), pp. 5–9
V. Chaurasia, S. Pal, B. Tiwari, Prediction of benign and malignant breast cancer using data mining techniques. J. Algorithms Comput. Technol. 12(2), 119–126 (2018)
N. Fatima, L. Liu, S. Hong, H. Ahmed, Prediction of breast cancer, comparative review of machine learning techniques, and their analysis. IEEE Access 8, 150360–150376 (2020)
A. Toprak, Extreme learning machine (ELM)-based classification of benign and malignant cells in breast cancer. Med. Sci. Monitor Int. Med. J. Exp. Clin. Res. 24, 6537 (2018)
D.S. Jacob, R. Viswan, V. Manju, L. PadmaSuresh, S. Raj, A survey on breast cancer prediction using data mining techniques, in 2018 Conference on Emerging Devices and Smart Systems (ICEDSS) (IEEE, 2018, March), pp. 256–258
T. Padhi, P. Kumar, Breast cancer analysis using WEKA, in 2019 9th International Conference on Cloud Computing, Data Science and Engineering (Confluence) (IEEE, 2019, January), pp. 229–232
T. Thomas, N. Pradhan, V.S. Dhaka, Comparative analysis to predict breast cancer using machine learning algorithms: a survey, in 2020 International Conference on Inventive Computation Technologies (ICICT) (IEEE, 2020, February), pp. 192–196
F. Livingston, Implementation of Breiman’s random forest machine learning algorithm. ECE591Q Mach. Learn. J. Paper 1–13 (2005)
R. Mitchell, E. Frank, Accelerating the XGBoost algorithm using GPU computing. PeerJ Comput. Sci. 3, e127 (2017)
M. Shalini, S. Radhika, Machine learning techniques for prediction from various breast cancer datasets, in 2020 Sixth International Conference on Bio Signals, Images, and Instrumentation (ICBSII) (IEEE, 2020, February), pp. 1–5
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, July), pp. 1–4
S. Marne, S. Churi, M. Marne, Predicting breast cancer using effective classification with decision tree and k means clustering technique, in 2020 International Conference on Emerging Smart Computing and Informatics (ESCI) (IEEE, 2020, March), pp. 39–42
Y. Khourdifi, M. Bahaj, Applying best machine learning algorithms for breast cancer prediction and classification, in 2018 International Conference on Electronics, Control, Optimization and Computer Science (ICECOCS) (IEEE, 2018, December), pp. 1–5
M.S. Yarabarla, L.K. Ravi, A. Sivasangari, Breast cancer prediction via machine learning, in 2019 3rd International Conference on Trends in Electronics and Informatics (ICOEI) (IEEE, 2019, April), pp. 121–124
P.S.S. Varma, S. Kumar, K.S.V. Reddy, Machine learning based breast cancer visualization and classification, in 2021 International Conference on Innovative Trends in Information Technology (ICITIIT) (IEEE, 2021, February), pp. 1–6
A. Sharma, S. Kulshrestha, S. Daniel, Machine learning approaches for breast cancer diagnosis and prognosis, in 2017 International Conference on Soft Computing and its Engineering Applications (icSoftComp) (IEEE, 2017, December), pp. 1–5
S. Sharma, A. Aggarwal, T. Choudhury, Breast cancer detection using machine learning algorithms, in 2018 International Conference on Computational Techniques, Electronics and Mechanical Systems (CTEMS) (IEEE, 2018, December), pp. 114–118
G.I. Webb, E. Keogh, R. Miikkulainen, Naïve Bayes. Encyclopedia Mach. Learn. 15, 713–714 (2010)
T. Vijayakumar, Posed inverse problem rectification using novel deep convolutional neural network. J. Innov. Image Process. (JIIP) 2(03), 121–127 (2020)
A.P. Pandian, Review on image recoloring methods for efficient naturalness by coloring data modeling methods for low visual deficiency. J. Artif. Intell. 3(03), 169–183 (2021)
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.
About this paper
Cite this paper
Islam, T., Kundu, A., Islam Khan, N., Chandra Bonik, C., Akter, F., Jihadul Islam, M. (2022). Machine Learning Approaches to Predict Breast Cancer: Bangladesh Perspective. In: Karuppusamy, P., García Márquez, F.P., Nguyen, T.N. (eds) Ubiquitous Intelligent Systems. ICUIS 2021. Smart Innovation, Systems and Technologies, vol 302. Springer, Singapore. https://doi.org/10.1007/978-981-19-2541-2_23
Download citation
DOI: https://doi.org/10.1007/978-981-19-2541-2_23
Published:
Publisher Name: Springer, Singapore
Print ISBN: 978-981-19-2540-5
Online ISBN: 978-981-19-2541-2
eBook Packages: Intelligent Technologies and RoboticsIntelligent Technologies and Robotics (R0)