Top

Automatic Control and Computer Sciences

Published in:

01-12-2023

BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning

Authors: Kuljeet Singh, Sourabh Shastri, Sachin Kumar, Vibhakar Mansotra

Published in: Automatic Control and Computer Sciences | Issue 6/2023

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

Breast cancer is a divergent and prominent cancer that is responsible for the morbidity and mortality of women throughout the world. This paper aims at early detection and accurate diagnosis of this fatal disease, which is one of the most important steps in breast cancer treatment. Therefore, various nested ensemble machine learning techniques are used to help doctors determine breast cancer at an early stage. The two-layer nested ensemble model has been proposed, which encompasses stacking and voting techniques to detect benign and malignant breast cancer tumors. A total of four two-layer nested ensemble models have been proposed. S(NaiveBayes)-V(3-Meta_Learner), S(BayesNet)-V(3-Meta_Learner), S(NaiveBayes)-V(4-Meta_Learner), and S(BayesNet)-V(4-Meta_Learner) have been designed to contain base learners and meta learners. The experiments have been conducted with the k-fold cross-validation technique for model evaluation. The proposed model is capable of classifying benign and malignant breast cancer tumors with 99.50% accuracy. The aforementioned four models have been compared with previous works in terms of classification accuracy, ROC, recall, precision, TP rate, FP rate, and F1 measure. The experiments showed that the proposed two-layer nested ensemble model S(BayesNet)-V(4-Meta_Learner) performed better than the other three models mentioned supra and competed with all the previously published works. This would help the scientific community and health practitioners diagnose breast cancer with early and accurate results.

previous article Study of an Automatic Marking Algorithm for Subjective Questions in College English Exams Based on Deep Learning

Dora, L., Agrawal, S., Panda, R., and Abraham, A., Optimal breast cancer classification using Gauss–Newton representation based algorithm, Expert Syst. Appl., 2017, vol. 85, pp. 134–145. https://doi.org/10.1016/j.eswa.2017.05.035CrossRef

Zheng, B., Yoon, S.W., and Lam, S.S., Breast cancer diagnosis based on feature extraction using a hybrid of K-means and support vector machine algorithms, Expert Syst. Appl., 2014, vol. 41, no. 4, pp. 1476–1482. https://doi.org/10.1016/j.eswa.2013.08.044CrossRef

Chen, Ch.-H., A hybrid intelligent model of analyzing clinical breast cancer data using clustering techniques with feature selection, Appl. Soft Comput., 2014, vol. 20, pp. 4–14. https://doi.org/10.1016/j.asoc.2013.10.024CrossRef

Muto, T., Bussey, H.J.R., and Morson, B.C., The evolution of cancer of the colon and rectum, Cancer, 1975, vol. 36, no. 6, pp. 2251–2270. https://doi.org/10.1002/cncr.2820360944CrossRef

Bhardwaj, A. and Tiwari, A., Breast cancer diagnosis using Genetically Optimized Neural Network model, Expert Syst. Appl., 2015, vol. 42, no. 10, pp. 4611–4620. https://doi.org/10.1016/j.eswa.2015.01.065CrossRef

Okagbue, H.I., Adamu, P.E., Oguntunde, P.E., Obasi, E.C.M., and Odetunmibi, O.A., Machine learning prediction of breast cancer survival using age, sex, length of stay, mode of diagnosis and location of cancer, Health Technol., 2021, vol. 11, no. 4, pp. 887–893. https://doi.org/10.1007/s12553-021-00572-4CrossRef

Zhou, Ch.-M., Xue, Q., Wang, Yi., Tong, J., Ji, M., and Yang, J.-J., Machine learning to predict the cancer-specific mortality of patients with primary non-metastatic invasive breast cancer, Surg. Today, 2021, vol. 51, no. 5, pp. 756–763. https://doi.org/10.1007/s00595-020-02170-9CrossRef

Globocan IAC, Globocan 2018: India factsheet—India Against Cancer, ICMR, 2022. http://cancerindia. org.in/globocan-2018-india-factsheet/. Cited July 16, 2022.

Christoyianni, I., Dermatas, E., and Kokkinakis, G., Fast detection of masses in computer-aided mammography, IEEE Signal Process. Mag., 2000, vol. 17, no. 1, pp. 54–64. https://doi.org/10.1109/79.814646CrossRef

10.

Elmore, J.G., Wells, C.K., Lee, C.H., Howard, D.H., and Feinstein, A.R., Variability in radiologists' interpretations of mammograms, New Engl. J. Med., 1994, vol. 331, no. 22, pp. 1493–1499. https://doi.org/10.1056/nejm199412013312206CrossRef

11.

Acharya, U.R., Ng, W.L., Rahmat, K., Sudarshan, V.K., Koh, J.E.W., Tan, J.H., Hagiwara, Yu., Yeong, Ch.H., and Ng, K.H., Data mining framework for breast lesion classification in shear wave ultrasound: A hybrid feature paradigm, Biomed. Signal Process. Control, 2017, vol. 33, pp. 400–410. https://doi.org/10.1016/j.bspc.2016.11.004CrossRef

12.

Kong, H., Lai, Z., Wang, X., and Liu, F., Breast cancer discriminant feature analysis for diagnosis via jointly sparse learning, Neurocomputing, 2016, vol. 177, pp. 198–205. https://doi.org/10.1016/j.neucom.2015.11.033CrossRef

13.

Örkcü, H.H. and Bal, H., Comparing performances of backpropagation and genetic algorithms in the data classification, Expert Syst. Appl., 2011, vol. 38, no. 4, pp. 3703–3709. https://doi.org/10.1016/j.eswa.2010.09.028CrossRef

14.

Breast cancer facts and statistics, Breast Cancer Care, 2022. https://www.breastcancer.org/facts-statistics. Cited July 19, 2022.

15.

Goodman, D.E., Boggess, L.C., and Watkins, A.B., Artificial immune system classification of multiple-class problems, Proc. Intell. Eng. Syst., 2022, vol. 12, pp. 179–184. https://www.semanticscholar.org/paper/artificial-immune-system-classification-of-multiple-Goodman-Boggess/4b0a63a93b6b1b9c19c83c31f163569320352b3d.

16.

Cevikalp, H., Triggs, B., Yavuz, H.S., Küçük, Ya., Küçük, M., and Barkana, A., Large margin classifiers based on affine hulls, Neurocomputing, 2010, vol. 73, nos. 16–18, pp. 3160–3168. https://doi.org/10.1016/j.neucom.2010.06.018CrossRef

17.

Li, D.-Ch. and Liu, Ch.-W., A class possibility based kernel to increase classification accuracy for small data sets using support vector machines, Expert Syst. Appl., 2010, vol. 37, no. 4, pp. 3104–3110. https://doi.org/10.1016/j.eswa.2009.09.019CrossRef

18.

Stoean, R. and Stoean, C., Modeling medical decision making by support vector machines, explaining by rules of evolutionary algorithms with feature selection, Expert Syst. Appl., 2013, vol. 40, no. 7, pp. 2677–2686. https://doi.org/10.1016/j.eswa.2012.11.007MathSciNetCrossRef

19.

Koloseni, D. and Luukka, P., Differential evolution based nearest prototype classifier with optimized distance measures and GOWA, Intelligent Systems’2014, Angelov, P. et al., Eds., Advances in Intelligent Systems and Computing, vol. 322, Cham: Springer, 2014, pp. 753–763. https://doi.org/10.1007/978-3-319-11313-5_66

20.

Chen, H.L., Yang, B., Wang, S., Wang, G., Liu, D.Yo., Li, H.Zh., and Liu, W.B., Towards an optimal support vector machine classifier using a parallel particle swarm optimization strategy, Appl. Math. Comput., 2014, vol. 239, pp. 180–197. https://doi.org/10.1016/j.amc.2014.04.039MathSciNetCrossRef

21.

Wang, H., Zheng, B., Yoon, S.W., and Ko, H.S., A support vector machine-based ensemble algorithm for breast cancer diagnosis, Eur. J. Oper. Res., 2018, vol. 267, no. 2, pp. 687–699. https://doi.org/10.1016/j.ejor.2017.12.001MathSciNetCrossRefMATH

22.

Wang, D., Wan, S., and Guizani, N., Context-based probability neural network classifiers realized by genetic optimization for medical decision making, Multimedia Tools Appl., 2018, vol. 77, no. 17, pp. 21995–22006. https://doi.org/10.1007/s11042-018-5631-3CrossRef

23.

Huang, M.-W., Chen, Ch.-W., Lin, W.-Ch., Ke, Sh.-W., and Tsai, Ch.-F., SVM and SVM ensembles in breast cancer prediction, PLoS ONE, 2017, vol. 12, no. 1, p. e0161501. https://doi.org/10.1371/journal.pone.0161501CrossRef

24.

Rosales-Pérez, A., Escalante, H.J., Gonzalez, J.A., Reyes-Garcia, C., and Coello Coello, C.A., Bias and variance multi-objective optimization for support vector machines model selection, Pattern Recognition and Image Analysis. IbPRIA, Sanches, J.M., Micó, L., and Cardoso, J.S., Eds., Lecture Notes in Computer Science, vol. 7887, Berlin: Springer, 2013, pp. 108–116. https://doi.org/10.1007/978-3-642-38628-2_12CrossRef

25.

Hassan, A.R. and Hassan Bhuiyan, M.I., Automatic sleep scoring using statistical features in the EMD domain and ensemble methods, Biocybernetics Biomed. Eng., 2016, vol. 36, no. 1, pp. 248–255. https://doi.org/10.1016/j.bbe.2015.11.001CrossRef

26.

Hassan, A.R., Computer-aided obstructive sleep apnea detection using normal inverse Gaussian parameters and adaptive boosting, Biomed. Signal Process. Control, 2016, vol. 29, pp. 22–30. https://doi.org/10.1016/j.bspc.2016.05.009CrossRef

27.

Ting, K.M. and Witten, I.H., Issues in stacked generalization, J. Artif. Intell. Res., 1999, vol. 10, pp. 271–289. https://doi.org/10.1613/jair.594CrossRefMATH

28.

Witten, I.H., Frank, E., and Hall, M.A., Data Mining: Practical Machine Learning Tools and Techniques, Amsterdam: Elsevier Science, 2005, 2nd ed.MATH

29.

Džeroski, S. and Ženko, B., Is combining classifiers with stacking better than selecting the best one?, Mach. Learn., 2004, vol. 54, no. 3, pp. 255–273. https://doi.org/10.1023/b:mach.0000015881.36452.6eCrossRefMATH

30.

Ram, S. and Gupta, S., Building machine learning based diseases diagnosis system considering various features of datasets, Emerging Trends in Expert Applications and Security, Rathore, V., Worring, M., Mishra, D., Joshi, A., and Maheshwari, S., Eds., Advances in Intelligent Systems and Computing, vol. 841, Singapore: Springer, 2019, pp. 147–155. https://doi.org/10.1007/978-981-13-2285-3_19

31.

Mihaylov, I., Nisheva, M., and Vassilev, D., Machine learning techniques for survival time prediction in breast cancer, Artificial Intelligence: Methodology, Systems, and Applications, Agre, G., van Genabith, J., and Declerck, T., Eds., Lecture Notes in Computer Science, vol. 11089, Cham: Springer, 2018, pp. 186–194. https://doi.org/10.1007/978-3-319-99344-7_17

32.

Selvathi, D. and Aarthy Poornila, A., Deep learning techniques for breast cancer detection using medical image analysis, Biologically Rationalized Computing Techniques for Image Processing Applications, Hemanth, J. and Balas, V., Eds., Lecture Notes in Computational Vision and Biomechanics, vol. 25, Cham: Springer, 2018, pp. 159–186. https://doi.org/10.1007/978-3-319-61316-1_8

33.

Li, X., Qin, G., He, Q., Sun, L., Zeng, H., He, Z., Chen, W., Zhen, X., and Zhou, L., Digital breast tomosynthesis versus digital mammography: Integration of image modalities enhances deep learning-based breast mass classification, Eur. Radiology, 2020, vol. 30, no. 2, pp. 778–788. https://doi.org/10.1007/s00330-019-06457-5CrossRef

34.

Chougrad, H., Zouaki, H., and Alheyane, O., Deep convolutional neural networks for breast cancer screening, Comput. Methods Programs Biomedicine, 2018, vol. 157, pp. 19–30. https://doi.org/10.1016/j.cmpb.2018.01.011CrossRef

35.

Kate, R.J. and Nadig, R., Stage-specific predictive models for breast cancer survivability, Int. J. Med. Inf., 2017, vol. 97, pp. 304–311. https://doi.org/10.1016/j.ijmedinf.2016.11.001CrossRef

36.

Asri, H., Mousannif, H., Al Moatassime, H., and Noel, T., Using machine learning algorithms for breast cancer risk prediction and diagnosis, Procedia Comput. Sci., 2016, vol. 83, pp. 1064–1069. https://doi.org/10.1016/j.procs.2016.04.224CrossRef

37.

Samala, R.K., Chan, H.-P., Hadjiiski, L., Helvie, M.A., Richter, C.D., and Cha, K.H., Breast cancer diagnosis in digital breast tomosynthesis: Effects of training sample size on multi-stage transfer learning using deep neural nets, IEEE Trans. Med. Imaging, 2019, vol. 38, no. 3, pp. 686–696. https://doi.org/10.1109/tmi.2018.2870343CrossRef

38.

U.I.M.L. Repository, Machine Learning Repository, 2010. http://archive.ics.uci.edu/ml/index.php. Cited July 19, 2022.

39.

Shukla, S., Jain, P.K., Babu, Ch.R., and Pamula, R., A multivariate regression model for identifying, analyzing and predicting crimes, Wireless Personal Commun., 2020, vol. 113, no. 4, pp. 2447–2461. https://doi.org/10.1007/s11277-020-07335-wCrossRef

40.

Friedman, N., Geiger, D., and Goldszmidt, M., Bayesian network classifiers, Mach. Learn., 1997, vol. 29, no. 2/3, pp. 131–163. https://doi.org/10.1023/a:1007465528199CrossRefMATH

41.

Shastri, S., Kour, P., Kumar, S., Singh, K., Sharma, A., and Mansotra, V., A nested stacking ensemble model for predicting districts with high and low maternal mortality ratio (MMR) in India, Int. J. Inf. Technol., 2021, vol. 13, no. 2, pp. 433–446. https://doi.org/10.1007/s41870-020-00560-3CrossRef

42.

Saito, T. and Rehmsmeier, M., The precision-recall plot is more informative than the ROC plot when evaluating binary classifiers on imbalanced datasets, PLoS ONE, 2015, vol. 10, no. 3, p. e0118432. https://doi.org/10.1371/journal.pone.0118432CrossRef

43.

Abdar, M., Zomorodi-Moghadam, M., Zhou, X., Gururajan, R., Tao, X., Barua, P., and Gururajan, R., A new nested ensemble technique for automated diagnosis of breast cancer, Pattern Recognit. Lett., 2020, vol. 132, pp. 123–131. https://doi.org/10.1016/j.patrec.2018.11.004CrossRef

44.

Lu, H., Wang, H., and Yoon, S.W., A dynamic gradient boosting machine using genetic optimizer for practical breast cancer prognosis, Expert Syst. Appl., 2019, vol. 116, pp. 340–350. https://doi.org/10.1016/j.eswa.2018.08.040CrossRef

45.

Maglogiannis, I., Zafiropoulos, E., and Anagnostopoulos, I., An intelligent system for automated breast cancer diagnosis and prognosis using SVM based classifiers, Appl. Intell., 2009, vol. 30, no. 1, pp. 24–36. https://doi.org/10.1007/s10489-007-0073-zCrossRef

46.

Sáez, J., Derrac, J., Luengo, J., and Herrera, F., Statistical computation of feature weighting schemes through data estimation for nearest neighbor classifiers, Pattern Recognit., 2014, vol. 47, no. 12, pp. 3941–3948. https://doi.org/10.1016/j.patcog.2014.06.012CrossRef

47.

Lim, C.K. and Chan, C.S., A weighted inference engine based on interval-valued fuzzy relational theory, Expert Syst. Appl., 2015, vol. 42, no. 7, pp. 3410–3419. https://doi.org/10.1016/j.eswa.2014.12.025CrossRef

48.

Aličković, E. and Subasi, A., Breast cancer diagnosis using GA feature selection and Rotation Forest, Neural Comput. Appl., 2017, vol. 28, no. 4, pp. 753–763. https://doi.org/10.1007/s00521-015-2103-9CrossRef

49.

Nilashi, M., Ibrahim, O., Ahmadi, H., and Shahmoradi, L., A knowledge-based system for breast cancer classification using fuzzy logic method, Telematics Inf., 2017, vol. 34, no. 4, pp. 133–144. https://doi.org/10.1016/j.tele.2017.01.007CrossRef

50.

Sanchez, A., Soguero-Ruiz, C., Mora-Jiménez, I., Rivas-Flores, F.J., Lehmann, D., and Rubio-Sánchez, M., Scaled radial axes for interactive visual feature selection: A case study for analyzing chronic conditions, Expert Syst. Appl., 2018, vol. 100, pp. 182–196. https://doi.org/10.1016/j.eswa.2018.01.054CrossRef

51.

Suresh, A. and Varatharajan, R., Recognition of pivotal instances from uneven set boundary during classification, Multimedia Tools Appl., 2018, vol. 77, no. 20, pp. 27075–27088. https://doi.org/10.1007/s11042-018-5905-9CrossRef

52.

Alshayeji, M.H., Ellethy, H., Abed, S., and Gupta, R., Computer-aided detection of breast cancer on the Wisconsin dataset: An artificial neural networks approach, Biomed. Signal Process. Control, 2022, vol. 71, p. 103141. https://doi.org/10.1016/j.bspc.2021.103141CrossRef

53.

Sannasi Chakravarthy, S.R., Bharanidharan, N., and Rajaguru, H., Deep learning-based metaheuristic weighted k-nearest neighbor algorithm for the severity classification of breast cancer, IRBM, 2023, vol. 44, no. 3, p. 100749. https://doi.org/10.1016/j.irbm.2022.100749CrossRef

Title: BC-Net: Early Diagnostics of Breast Cancer Using Nested Ensemble Technique of Machine Learning
Authors: Kuljeet Singh
Sourabh Shastri
Sachin Kumar
Vibhakar Mansotra
Publication date: 01-12-2023
Publisher: Pleiades Publishing
Published in: Automatic Control and Computer Sciences / Issue 6/2023
Print ISSN: 0146-4116
Electronic ISSN: 1558-108X
DOI: https://doi.org/10.3103/S0146411623060093

Springer Professional

Abstract

Please log in to get access to your license.

Other articles of this Issue 6/2023

Evaluation of Optimized Inner and Outer Loop Controllers for 4X Flyer under Faulty Actuators

Classification of Services through Feature Selection and Machine Learning in 5G Networks

Color Models Aware Dynamic Feature Extraction for Forest Fire Detection Using Machine Learning Classifiers

Air Pollutants Classification Using Optimized Neural Network Based on War Strategy Optimization Algorithm

Flexible Nodes Exclusion and Inclusion Capability Migration Model for Hybrid Software-Defined Network

QAM Modulation Based on Lowest Energy Consumption in Passive CRFID