Download PDF
Methods Inf Med 2022; 61(03/04): 068-083
DOI: 10.1055/s-0042-1751043
Original Article

Breast Cancer Subtypes Classification with Hybrid Machine Learning Model

Suvobrata Sarkar
1   Department of Computer Science and Engineering, Dr. B.C. Roy Engineering College, Durgapur, West Bengal, India
,
Kalyani Mali
2   Department of Computer Science and Engineering, University of Kalyani, Kalyani, West Bengal, India
› Author Affiliations Funding None.
› Further Information
    Permissions and Reprints

Abstract

Background Breast cancer is the most prevailing heterogeneous disease among females characterized with distinct molecular subtypes and varied clinicopathological features. With the emergence of various artificial intelligence techniques especially machine learning, the breast cancer research has attained new heights in cancer detection and prognosis.

Objective Recent development in computer driven diagnostic system has enabled the clinicians to improve the accuracy in detecting various types of breast tumors. Our study is to develop a computer driven diagnostic system which will enable the clinicians to improve the accuracy in detecting various types of breast tumors.

Methods In this article, we proposed a breast cancer classification model based on the hybridization of machine learning approaches for classifying triple-negative breast cancer and non-triple negative breast cancer patients with clinicopathological features collected from multiple tertiary care hospitals/centers.

Results The results of genetic algorithm and support vector machine (GA-SVM) hybrid model was compared with classics feature selection SVM hybrid models like support vector machine-recursive feature elimination (SVM-RFE), LASSO-SVM, Grid-SVM, and linear SVM. The classification results obtained from GA-SVM hybrid model outperformed the other compared models when applied on two distinct hospital-based datasets of patients investigated with breast cancer in North West of African subcontinent. To validate the predictive model accuracy, 10-fold cross-validation method was applied on all models with the same multicentered datasets. The model performance was evaluated with well-known metrics like mean squared error, logarithmic loss, F1-score, area under the ROC curve, and the precision–recall curve.

Conclusion The hybrid machine learning model can be employed for breast cancer subtypes classification that could help the medical practitioners in better treatment planning and disease outcome.

Keywords

triple-negative breast cancer - clinicopathological parameters - hybrid machine learning models - classification - genetic algorithm - support vector machine


Publication History

Received: 31 December 2021

Accepted: 11 May 2022

Article published online:
12 September 2022

© 2022. Thieme. All rights reserved.

Georg Thieme Verlag KG
Rüdigerstraße 14, 70469 Stuttgart, Germany

 

  • References

  • 1 Sung H, Ferlay J, Siegel RL. et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin 2021; 71 (03) 209-249
    Google Scholar
  • 2 World Health Organization. Breast Cancer. Accessed March 26, 2021 at: https://www.who.int/news-room/fact-sheets/detail/breast-cancer
    PubMedGoogle Scholar
  • 3 Ferroni P, Zanzotto FM, Riondino S, Scarpato N, Guadagni F, Roselli M. Breast cancer prognosis using a machine learning approach. Cancers (Basel) 2019; 11 (03) 328
    CrossrefPubMedGoogle Scholar
  • 4 Kim W, Kim KS, Lee JE. et al. Development of novel breast cancer recurrence prediction model using support vector machine. J Breast Cancer 2012; 15 (02) 230-238
    CrossrefPubMedGoogle Scholar
  • 5 Kourou K, Exarchos TP, Exarchos KP, Karamouzis MV, Fotiadis DI. Machine learning applications in cancer prognosis and prediction. Comput Struct Biotechnol J 2014; 13: 8-17
    CrossrefPubMedGoogle Scholar
  • 6 Tao M, Song T, Du W. et al. Classifying breast cancer subtypes using multiple kernel learning based on omics data. Genes (Basel) 2019; 10 (03) 200
    CrossrefPubMedGoogle Scholar
  • 7 Zolbanin HM, Delen D, Zadeh AH. Predicting overall survivability in comorbidity of cancers: a data mining approach. Decis Support Syst 2015; 74: 150-161
    CrossrefPubMedGoogle Scholar
  • 8 Chen D, Xing K, Henson D, Sheng L, Schwartz AM, Cheng X. Developing prognostic systems of cancer patients by ensemble clustering. J Biomed Biotechnol 2009; 2009: 632786
    CrossrefPubMedGoogle Scholar
  • 9 Shah SM, Khan RA, Arif S, Sajid U. Artificial intelligence for breast cancer analysis: trends & directions. Comput Biol Med 2022; 142: 105221
    CrossrefPubMedGoogle Scholar
  • 10 Saber A, Sakr M, Abo-Seida OM. et al. A novel deep-learning model for automatic detection and classification of breast cancer using the transfer-learning technique. IEEE Access 2021; 9: 71194-71209
    CrossrefPubMedGoogle Scholar
  • 11 Anderson P, Gadgil R, Johnson WA, Schwab E, Davidson JM. Reducing variability of breast cancer subtype predictors by grounding deep learning models in prior knowledge. Comput Biol Med 2021; 138: 104850
    CrossrefPubMedGoogle Scholar
  • 12 Zhao S, Wang P, Heidari AA, Chen H, He W, Xu S. Performance optimization of salp swarm algorithm for multi-threshold image segmentation: comprehensive study of breast cancer microscopy. Comput Biol Med 2021; 139: 105015
    CrossrefPubMedGoogle Scholar
  • 13 Liu L, Zhao D, Yu F. et al. Performance optimization of differential evolution with slime mould algorithm for multilevel breast cancer image segmentation. Comput Biol Med 2021; 138: 104910
    CrossrefPubMedGoogle Scholar
  • 14 Huang H, Feng X, Zhou S. et al. A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features. BMC Bioinformatics 2019; 20 (08, Suppl 8): 290
    CrossrefPubMedGoogle Scholar
  • 15 Tu J, Lin A, Chen H. et al. Predict the entrepreneurial intention of fresh graduate students based on an adaptive support vector machine framework. Math Probl Eng 2019; 2019: 1-16
    CrossrefPubMedGoogle Scholar
  • 16 Shahbakhi M, Far DT, Tahami E. Speech analysis for diagnosis of Parkinson's disease using genetic algorithm and support vector machine. J Biomed Sci Eng 2014; 7 (04) 147-156
    CrossrefPubMedGoogle Scholar
  • 17 Chen X, Liu K, Cai J. et al. Identification of heavy metal-contaminated Tegillarca granosa using infrared spectroscopy. Anal Methods 2015; 7 (05) 2172-2181
    CrossrefPubMedGoogle Scholar
  • 18 Sarkar JP, Saha I, Sarkar A, Maulik U. Machine learning integrated ensemble of feature selection methods followed by survival analysis for predicting breast cancer subtype specific miRNA biomarkers. Comput Biol Med 2021; 131: 104244
    CrossrefPubMedGoogle Scholar
  • 19 Ben Azzouz F, Michel B, Lasla H. et al. Development of an absolute assignment predictor for triple-negative breast cancer subtyping using machine learning approaches. Comput Biol Med 2021; 129: 104171
    CrossrefPubMedGoogle Scholar
  • 20 Howlader N, Noone AM, Krapcho M. et al. SEER*Explorer. Breast: Recent Trends in SEER Age-Adjusted Incidence Rates, 2000–2018, by Race/Ethnicity, Delay-Adjusted SEER Incidence Rate, Female, Ages 15–39, All Stages. Bethesda, MD: National Cancer Institute; 2021
    Google Scholar
  • 21 Trivers KF, Lund MJ, Porter PL. et al. The epidemiology of triple-negative breast cancer, including race. Cancer Causes Control 2009; 20 (07) 1071-1082
    CrossrefPubMedGoogle Scholar
  • 22 Amirikia KC, Mills P, Bush J, Newman LA. Higher population-based incidence rates of triple-negative breast cancer among young African-American women: implications for breast cancer screening recommendations. Cancer 2011; 117 (12) 2747-2753
    CrossrefPubMedGoogle Scholar
  • 23 Stead LA, Lash TL, Sobieraj JE. et al. Triple-negative breast cancers are increased in black women regardless of age or body mass index. Breast Cancer Res 2009; 11 (02) R18
    CrossrefPubMedGoogle Scholar
  • 24 Stark A, Kleer CG, Martin I. et al. African ancestry and higher prevalence of triple-negative breast cancer: findings from an international study. Cancer 2010; 116 (21) 4926-4932
    CrossrefPubMedGoogle Scholar
  • 25 Nedeljković M, Damjanović A. Mechanisms of chemotherapy resistance in triple-negative breast cancer-how we can rise to the challenge. Cells 2019; 8 (09) 957
    CrossrefPubMedGoogle Scholar
  • 26 Biostudies. BioStudies – one package for all the data supporting a study. Available at: https://www.ebi.ac.uk/biostudies/
    PubMedGoogle Scholar
  • 27 Mouh FZ, Slaoui M, Razine R, El Mzibri M, Amrani M. Clinicopathological, treatment and event-free survival characteristics in a Moroccan population of triple-negative breast cancer. Breast Cancer (Auckl) 2020; 14: 1178223420906428
    CrossrefPubMedGoogle Scholar
  • 28 Biostudies. Clinicopathological, treatment and event-free survival characteristics in a moroccan population of triple-negative breast cancer. Available at: https://www.ebi.ac.uk/biostudies/studies/S-EPMC7218339?query=Clinicopathological%2C%20Treatment%20and%20Event-Free%20Survival%20Characteristics%20in%20a%20Moroccan%20Population%20of%20Triple-Negative%20Breast%20Cancer%20Fatima%20Zahra%20Mouh
    PubMedGoogle Scholar
  • 29 Adeniji AA, Dawodu OO, Habeebu MY. et al. Distribution of breast cancer subtypes among Nigerian women and correlation to the risk factors and clinicopathological characteristics. World J Oncol 2020; 11 (04) 165-172
    CrossrefPubMedGoogle Scholar
  • 30 Biostudies. Distribution of breast cancer subtypes among Nigerian women and correlation to the risk factors and clinicopathological characteristics. Available at: https://www.ebi.ac.uk/biostudies/studies/S-EPMC7430856?query=distribution%20of%20breast%20cancer%20subtype%20among%20nigerian%20women
    PubMedGoogle Scholar
  • 31 Zeng X, Chen YW, Tao C. Feature Selection Using Recursive Feature Elimination for Handwritten Digit Recognition. 2009. Paper presented at: Fifth International Conference on Intelligent Information Hiding and Multimedia Signal Processing; 12–14 September 2009; Japan
    PubMedGoogle Scholar
  • 32 Vapnik V, Lerner A. Pattern recognition using generalized portrait method. Autom Remote Control 1963; 24: 774-780
    PubMedGoogle Scholar
  • 33 Goldberg DE. Genetic Algorithms in Search, Optimization and Machine Learning. New York: Addison-Wesley; 1989
    Google Scholar
  • 34 Davis L. Handbook of Genetic Algorithms. Edition. New York: Van Nostrand Reinhold; 1991
    Google Scholar
  • 35 Michalewicz Z. Genetic Algorithms+Data Structures, Evolution Programs. New York: Springer; 1992
    CrossrefGoogle Scholar
  • 36 Filho JLR, Treleaven PC, Alippi C. Genetic algorithm programming environments. IEEE Computer 1994; 27: 28-43
    CrossrefPubMedGoogle Scholar
  • 37 Scikit-learn. Machine Learning in Python. Available at: https://scikit-learn.org/stable/
    PubMedGoogle Scholar
  • 38 Mandrekar JN. Receiver operating characteristic curve in diagnostic test assessment. J Thorac Oncol 2010; 5 (09) 1315-1316
    CrossrefPubMedGoogle Scholar
  • 39 Huang ML, Hung YH, Lee WM, Li RK, Jiang BR. SVM-RFE based feature selection and Taguchi parameters optimization for multiclass SVM classifier. ScientificWorldJournal 2014; 2014: 795624
    CrossrefPubMedGoogle Scholar
  • 40 Algamal ZY, Lee MH. Penalized logistic regression with the adaptive LASSO for gene selection in high-dimensional cancer classification. Expert Syst Appl 2015; 42 (23) 9326-9332
    CrossrefPubMedGoogle Scholar
  • 41 Nursabillilah A, Nor A, Rosli B. Comparison of microarray breast cancer classification using support vector machine and logistic regression with LASSO and boruta feature selection. Indonesian J Electrical Engineering Comp Sci 2020; 20 (02) 712-719
    CrossrefPubMedGoogle Scholar
  • 42 Huang CL, Liao HC, Chen MC. Prediction model building and feature selection with support vector machines in breast cancer diagnosis. Expert Syst Appl 2008; 34 (01) 578-587
    CrossrefPubMedGoogle Scholar
  • 43 Akay MF. Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst Appl 2009; 36 (02) 3240-3247
    CrossrefPubMedGoogle Scholar
  • 44 Asri H, Mousannif H, Moatassime AH. et al. Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput Sci 2016; 83: 1064-1069
    CrossrefPubMedGoogle Scholar
  • 45 Charlton KE, Rose D. Nutrition among older adults in Africa: the situation at the beginning of the millennium. J Nutr 2001; 131 (09) 2424S-2428S
    CrossrefPubMedGoogle Scholar
  • 46 Huang MW, Chen CW, Lin WC, Ke SW, Tsai CF. SVM and SVM ensembles in breast cancer prediction. PLoS One 2017; 12 (01) e0161501
    CrossrefPubMedGoogle Scholar
  • 47 Alba E, Garcia-Nieto J, Jourdan L. et al. Gene selection in cancer classification using PSO/SVM and GA/SVM hybrid algorithms. Paper presented at: 2007 IEEE Congress on Evolutionary Computation conference proceedings; September 25–28, 2007; Singapore
    PubMedGoogle Scholar
  • 48 Moteghaed NY, Maghooli K, Garshasbi M. Improving classification of cancer and mining biomarkers from gene expression profiles using hybrid optimization algorithms and fuzzy support vector machine. J Med Signals Sens 2018; 8 (01) 1-11
    CrossrefPubMedGoogle Scholar
  • 49 Xu H, Chen T, Lv J. et al. A combined parallel genetic algorithm and support vector machine model for breast cancer detection. J Comp Methods Sci Engineering 2016; 16 (04) 773-785
    PubMedGoogle Scholar
  • 50 Aličković E, Subasi A. Breast cancer diagnosis using GA feature selection and Rotation Forest. Neural Comput Appl 2017; 28: 753-763
    CrossrefPubMedGoogle Scholar
  • 51 Xu Y, Ju L, Tong J, Zhou C, Yang J. supervised machine learning predictive analytics for triple-negative breast cancer death outcomes. OncoTargets Ther 2019; 12: 9059-9067
    CrossrefPubMedGoogle Scholar
  • 52 Castillo W, Melin O, Pedrycz P. Hybrid Intelligent Systems: Analysis and Design Studies in Fuzziness and Soft Computing. Berlin Heidelberg: Springer; 2007: 55-64
    CrossrefGoogle Scholar
  • 53 Vural S, Wang X, Guda C. Classification of breast cancer patients using somatic mutation profiles and machine learning approaches. BMC Syst Biol 2016; 10 (Suppl. 03) 62
    CrossrefPubMedGoogle Scholar
  • 54 Beykikhoshk A, Quinn TP, Lee SC, Tran T, Venkatesh S. DeepTRIAGE: interpretable and individualised biomarker scores using attention mechanism for the classification of breast cancer sub-types. BMC Med Genomics 2020; 13 (Suppl. 03) 20
    CrossrefPubMedGoogle Scholar
  • 55 Seo MK, Paik S, Kim S. An improved, assay platform agnostic, absolute single sample breast cancer subtype classifier. Cancers (Basel) 2020; 12 (12) 3506
    CrossrefPubMedGoogle Scholar
  • 56 Yu Z, Wang Z, Yu X, Zhang Z. RNA-Seq-based breast cancer subtypes classification using machine learning approaches. Comput Intell Neurosci 2020; 2020: 4737969
    PubMedGoogle Scholar
  • 57 Xie T, Wang Z, Zhao Q. et al. Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer. Front Oncol 2019; 9: 505
    CrossrefPubMedGoogle Scholar
  • 58 Wu J, Hicks C. Breast cancer type classification using machine learning. J Pers Med 2021; 11 (02) 61
    CrossrefPubMedGoogle Scholar
  • 59 Ma W, Zhao Y, Ji Y. et al. Breast cancer molecular subtype prediction by mammographic radiomic features. Acad Radiol 2019; 26 (02) 196-201
    CrossrefPubMedGoogle Scholar
  • 60 Peppercorn J, Perou CM, Carey LA. Molecular subtypes in breast cancer evaluation and management: divide and conquer. Cancer Invest 2008; 26 (01) 1-10
    CrossrefPubMedGoogle Scholar
  • 61 Huerta EB, Duval B, Hao JK. A Hybrid GA/SVM approach for gene selection and classification of microarray data. evo workshops 2006. LNCS 2006; 3907: 34-44
    PubMedGoogle Scholar
  • 62 Ngadi M, Nassih B, Hachimi H. et al. Genetic algorithms combined with support vector machine for breast cancer diagnosis. Paper presented at: International Workshop in Optimization and Applications Woa; 2016;17th-19th May 2016
    PubMedGoogle Scholar
  • 63 Resmini R, Silva L, Araujo AS, Medeiros P, Muchaluat-Saade D, Conci A. Combining genetic algorithms and SVM for breast cancer diagnosis using infrared thermography. Sensors (Basel) 2021; 21 (14) 4802
    CrossrefPubMedGoogle Scholar
  • 64 Wu T, Sultan LR, Tian J, Cary TW, Sehgal CM. Machine learning for diagnostic ultrasound of triple-negative breast cancer. Breast Cancer Res Treat 2019; 173 (02) 365-373
    CrossrefPubMedGoogle Scholar
  • 65 Turkki R, Byckhov D, Lundin M. et al. Breast cancer outcome prediction with tumour tissue images and machine learning. Breast Cancer Res Treat 2019; 177 (01) 41-52
    CrossrefPubMedGoogle Scholar
  • 66 Parshad R, Kazi M, Seenu V, Mathur S, Dattagupta S, Haresh KP. Suhani. Triple-negative breast cancers: are they always different from nontriple-negative breast cancers? An experience from a tertiary center in India. Indian J Cancer 2017; 54 (04) 658-663
    CrossrefPubMedGoogle Scholar
  • 67 Gogia A, Raina V, Deo SVS, Shukla NK, Mohanti BK. Triple-negative breast cancer: an institutional analysis. Indian J Cancer 2014; 51 (02) 163-166
    CrossrefPubMedGoogle Scholar
  • 68 Sharma D, Singh G. An institutional analysis of clinicopathological features of triple negative breast cancer. Indian J Cancer 2016; 53 (04) 566-568
    CrossrefPubMedGoogle Scholar
  • 69 Doval DC, Sharma A, Sinha R. et al. Immunohistochemical profile of breast cancer patients at a tertiary care hospital in New Delhi, India. Asian Pac J Cancer Prev 2015; 16 (12) 4959-4964
    CrossrefPubMedGoogle Scholar
  • 70 Sharma M, Sharma JD, Sarma A. et al. Triple negative breast cancer in people of North East India: critical insights gained at a regional cancer centre. Asian Pac J Cancer Prev 2014; 15 (11) 4507-4511
    CrossrefPubMedGoogle Scholar
  • 71 Weston AD, Hood L. Systems biology, proteomics, and the future of health care: toward predictive, preventative, and personalized medicine. J Proteome Res 2004; 3 (02) 179-196
    CrossrefPubMedGoogle Scholar
  • 72 Kuang F, Xu W, Zhang S. A novel hybrid KPCA and SVM with GA model for intrusion detection. Appl Soft Comput 2014; 18: 178-184
    CrossrefPubMedGoogle Scholar