Abstract
Breast cancer is one of the most common cancers among women’s worldwide, and it is a fact that most of the cases are discovered late. Several researchers have examined the prediction of breast cancer. Breast cancer poses a significant hazard to women. The deficiency of reliable predictive models really makes it challenging for clinicians to devise a treatment strategy that will help patients live longer. An automatic illness detection system assists medical personnel in diagnosing disease and provides a reliable, efficient and quick reaction while also lowering the danger of death. A Blended ensemble learning, which is an innovative approach, has been utilized for the classification of breast cancer and this model performs effectively for the base classifier in the prediction analysis. The performance of five machine learning techniques, namely support vector machine, K-nearest neighbors, decision tree Classifier, random forests, and logistic regression, are used as base learners in blended ensemble model. All the incorporated base learners (individually) and the final outcome of the Ensemble Learning are being compared in this study against several performance metrics namely accuracy, recall, precision and f1-score for the early prediction of Breast Cancer. There is a 98.14 percent noticeable improvement with the Ensemble Learning model compared to the basic learners.
This is a preview of subscription content, log in via an institution to check access.
Similar content being viewed by others
References
Agarap AFM (2018) [ACM Press the 2nd international conference—Phu Quoc Island, Viet Nam (2018.02.02–2018.02.04)] Proceedings of the 2nd international conference on machine learning and soft computing—ICMLSC '18—“On breast cancer detection”, pp 5–9
Akbugday B (2019) 2019 Medical Technologies Congress (TIPTEKNO)—Izmir, Turkey (2019.10.3–2019.10.5)] 2019 Medical Technologies Congress (TIPTEKNO)—“Classification of Breast Cancer Data Using Machine Learning Algorithms”, pp 1–4
Aslan MF, Celik Y, Sabanci K, Durdu A (2018) Breast cancer diagnosis by different machine learning methods using blood analysis data. Int J Intell Syst Appl Eng 6(4):289–293
Asri H, Mousannif H, Al Moatassime H, Noel T (2016) Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 83:1064–1069
Assiri AS, Nazir S, Velastin SA (2020) Breast tumor classification using an ensemble machine learning method. J Imaging 6(6):39
Breast Cancer (2018) Statistics, Approved by the Cancer.Net Editorial Board, 04/2017. [Online]. http://www.cancer.net/cancer-types/breast-cancer/statistics. Accessed 26 Aug 2018
Chauhan P, Swami A (2018) Breast cancer prediction using genetic algorithm based ensemble approach. In: 2018 9th international conference on computing, communication and networking technologies (ICCCNT), 2018, pp 1–8
Delen D (2009) Analysis of cancer data: a data mining approach. Expert Syst 26(1):100–112
Delen D, Walker G, Kadam A (2005) Predicting breast cancer survivability: a comparison of three data mining methods. Artif Intell Med 34(2):113–127
Dhiman G, Vinoth Kumar V, Kaur A, Sharma A (2021) DON: deep learning and optimization-based framework for detection of novel coronavirus disease using x-ray images. Interdiscip Sci Comput Life Sci 13:260–272
Eltalhi S, Kutrani H (2019) Breast cancer diagnosis and prediction using machine learning and data mining techniques: a review. IOSR J Dent Med Sci 18(4):85–94
Gupta MK, Chandra P (2020) A comprehensive survey of data mining. Int J Inf Technol 12:1–15
Gupta P, Shalini L (2018) Analysis of machine learning techniques for breast cancer prediction. Int J Eng Comput Sci 7(05):23891–23895
Huang Q, Chen Y, Liu L, Tao D, Li X (2020) On combining bi-clustering mining and AdaBoost for breast tumor classification. IEEE Trans Knowl Data Eng 32(4):728–738
Keles MK (2019) Breast cancer prediction and detection using data mining classification algorithms: a comparative study. Tehn Vjesn Tech Gazette 26(1):149–155
Khan S, Islam N, Jan Z, Din IU, Rodrigues JJ (2019) A novel deep learning based framework for the detection and classification of breast cancer using transfer learning. Pattern Recognit Lett 125:1–6
Kharya S, Soni S (2016) Weighted naive bayes classifier: a predictive model for breast cancer detection. Int J Comput Appl 133(9):32–37
Kumar D, Swathi P, Jahangir A, Sah NK, Vinothkumar V (2021) Intelligent speech processing technique for suspicious voice call identification using adaptive machine learning approach. In: Advances in computational intelligence and robotics, pp 372–380
Li L, Wu Y, Ou Y, Li Q, Zhou Y, Chen D (2017) [IEEE 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—Montreal, QC, Canada (2017.10.8–2017.10.13)] 2017 IEEE 28th annual international symposium on personal, indoor, and mobile radio communications (PIMRC)—“Research on machine learning algorithms and feature extraction for time series”, pp 1–5
Olson DL, Delen D (2008).“Advanced Data Mining Techniques”, Springer, 2008, ISBN: 978–3–540–76917–0.
Park SH, Han K (2018) Methodologic guide for evaluating clinical performance and effect of artificial intelligence technology for medical diagnosis and prediction. Radiology 286:171920
Salod Z, Singh Y (2019) Comparison of the performance of machine learning algorithms in breast cancer screening and detection: a protocol. J Public Health Res 8(3):jphr-2019
Sarveshvar MR, Gogoi A, Chaubey AK, Rohit S, Mahesh TR (2021a) Performance of different machine learning techniques for the prediction of heart diseases. In: 2021a International conference on forensics, analytics, big data, security (FABS), 2021, pp 1–4
Shahbaz M, Faruq S, Shaheen M, Masood SA (2012) Cancer diagnosis using data mining technology. Life Sci J 9(1):308–313
Shashikala HK, Mahesh TR, Vivek V, Sindhu MG, Saravanan C, Baig TZ (2021b) Early detection of spondylosis using point-based image processing techniques. In: 2021b International conference on recent trends on electronics, information, communication & technology (RTEICT), pp 655–659
Shrestha P, Singh A, Garg R, Sarraf I, Mahesh TR, Sindhu Madhuri G (2021c) Early stage detection of scoliosis using machine learning algorithms. In: 2021c International conference on forensics, analytics, big data, security (FABS), pp 1–4
Telsang VA, nd Hegde K (2020) Breast cancer prediction analysis using machine learning algorithms. In: 2020 International conference on communication, computing and industry 4.0 (C2I4)
Funding
No funds has been recieved to carry out this research work.
Author information
Authors and Affiliations
Corresponding author
Ethics declarations
Conflict of interest
The authors declare that they have no conflict of interest.
Human and animal rights
No animals and humans participated in this research.
Informed consent
No consent.
Additional information
Publisher's Note
Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.
Rights and permissions
About this article
Cite this article
Mahesh, T.R., Vinoth Kumar, V., Vivek, V. et al. Early predictive model for breast cancer classification using blended ensemble learning. Int J Syst Assur Eng Manag 15, 188–197 (2024). https://doi.org/10.1007/s13198-022-01696-0
Received:
Revised:
Accepted:
Published:
Issue Date:
DOI: https://doi.org/10.1007/s13198-022-01696-0