A Comprehensive Review: Bibliometric Analysis of Decision Tree-Based Approaches for Breast Cancer Prediction

Suhartono Suhartono, Syahiduz Zaman

Abstract


This paper aims to conduct a bibliometric analysis of scientific publications that discuss using the decision tree method for breast cancer prediction. Three hundred twenty-two documents from Scopus were collected for analysis using bibliometric indicators such as productivity and citations. The bibliometric analysis produces scientific mapping based on the keywords co-occurrence, co-authorship, and co-citation analysis to reflect the conceptual, social, and intellectual structure. The analysis of the evolution article found an exponential increase in citations and the number of authors in this study in 2005-2023, where China was the dominant country in conducting research. In the thematic map analysis, three research topics were produced, namely the medical field, the computer field, and the bioinformatics field. Research topics in using the decision tree method for breast cancer prediction are included in the computer field. This study suggests that research on using the decision tree method for breast cancer prediction is a research topic that needs improvement.

Full Text:

PDF

References


[1] A. El-Nabawy, N. A. Belal, and N. El-Bendary, ‘A cascade deep forest model for breast cancer subtype classification using multi-omics data’, Mathematics, vol. 9, no. 13, 2021, doi: 10.3390/math9131574.

[2] M. M. Alshammari, A. Almuhanna, and J. Alhiyafi, ‘Mammography image-based diagnosis of breast cancer using machine learning: A pilot study’, Sensors, vol. 22, no. 1, 2022, doi: 10.3390/s22010203.

[3] M. Botlagunta et al., ‘Classification and diagnostic prediction of breast cancer metastasis on clinical data using machine learning algorithms’, Sci. Rep., vol. 13, no. 1, 2023, doi: 10.1038/s41598-023-27548-w.

[4] G. Li, T. Fang, Y. Zhang, C. Liang, Q. Xiao, and J. Luo, ‘Predicting miRNA-disease associations based on graph attention network with multi-source information’, BMC Bioinformatics, vol. 23, no. 1, 2022, doi: 10.1186/s12859-022-04796-7.

[5] T. Xie et al., ‘Machine learning-based analysis of MR multiparametric radiomics for the subtype classification of breast cancer’, Front. Oncol., vol. 9, no. JUN, 2019, doi: 10.3389/fonc.2019.00505.

[6] F. K. Nasser and S. F. Behadili, ‘Breast Cancer Detection using Decision Tree and K-Nearest Neighbour Classifiers’, Iraqi J. Sci., vol. 63, no. 11, pp. 4987–5003, 2022, doi: 10.24996/ijs.2022.63.11.34.

[7] V. R. Mudunuru and L. A. Skrzypek, ‘A comparison of artificial neural network and decision trees with logistic regression as classification models for breast cancer survival’, Int. J. Math. Eng. Manag. Sci., vol. 5, no. 6, pp. 1170–1190, 2020, doi: 10.33889/IJMEMS.2020.5.6.089.

[8] X. Chen, C.-C. Zhu, and J. Yin, ‘Ensemble of decision tree reveals potential miRNA-disease associations’, PLoS Comput. Biol., vol. 15, no. 7, 2019, doi: 10.1371/journal.pcbi.1007209.

[9] J.-X. Tian and J. Zhang, ‘Breast cancer diagnosis using feature extraction and boosted C5.0 decision tree algorithm with penalty factor’, Math. Biosci. Eng., vol. 19, no. 3, pp. 2193–2205, 2022, doi: 10.3934/MBE.2022102.

[10] L. Dobrovska and O. Nosovets, ‘Development Of The Classifier Based On A Multilayer Perceptron Using Genetic Algorithm And Cart Decision Tree’, East.-Eur. J. Enterp. Technol., vol. 5, no. 9–113, pp. 82–90, 2021, doi: 10.15587/1729-4061.2021.242795.

[11] T. A. Assegie, R. L. Tulasi, and N. K. Kumar, ‘Breast cancer prediction model with decision tree and adaptive boosting’, IAES Int. J. Artif. Intell., vol. 10, no. 1, pp. 184–190, 2021, doi: 10.11591/ijai.v10.i1.pp184-190.

[12] L. Yang et al., ‘A decision tree-based prediction model for fluorescence in situ hybridization HER2 gene status in HER2 immunohistochemistry-2+ breast cancers: A 2538-case multicenter study on consecutive surgical specimens’, J. Cancer, vol. 9, no. 13, pp. 2327–2333, 2018, doi: 10.7150/jca.25586.

[13] M. Aria and C. Cuccurullo, ‘bibliometrix : An R-tool for comprehensive science mapping analysis’, J. Informetr., vol. 11, no. 4, pp. 959–975, Nov. 2017, doi: 10.1016/j.joi.2017.08.007.

[14] M. J. Page et al., ‘The PRISMA 2020 statement: an updated guideline for reporting systematic reviews’, Syst. Rev., vol. 10, no. 1, p. 89, Dec. 2021, doi: 10.1186/s13643-021-01626-4.

[15] F. Pelon et al., ‘Cancer-associated fibroblast heterogeneity in axillary lymph nodes drives metastases in breast cancer through complementary mechanisms’, Nat. Commun., vol. 11, no. 1, 2020, doi: 10.1038/s41467-019-14134-w.

[16] M. D. Ganggayah, N. A. Taib, Y. C. Har, P. Lio, and S. K. Dhillon, ‘Predicting factors for survival of breast cancer patients using machine learning techniques’, BMC Med. Inform. Decis. Mak., vol. 19, no. 1, p. 48, Dec. 2019, doi: 10.1186/s12911-019-0801-4.

[17] W. Yue, Z. Wang, H. Chen, A. Payne, and X. Liu, ‘Machine learning with applications in breast cancer diagnosis and prognosis’, Designs, vol. 2, no. 2, pp. 1–17, 2018, doi: 10.3390/designs2020013.

[18] S. B. Sakri, N. B. Abdul Rashid, and Z. Muhammad Zain, ‘Particle Swarm Optimization Feature Selection for Breast Cancer Recurrence Prediction’, IEEE Access, vol. 6, pp. 29637–29647, 2018, doi: 10.1109/ACCESS.2018.2843443.

[19] M. R. Mohebian, H. R. Marateb, M. Mansourian, M. A. Mañanas, and F. Mokarian, ‘A Hybrid Computer-aided-diagnosis System for Prediction of Breast Cancer Recurrence (HPBCR) Using Optimized Ensemble Learning’, Comput. Struct. Biotechnol. J., vol. 15, pp. 75–85, 2017, doi: 10.1016/j.csbj.2016.11.004.

[20] E. Rexhepaj et al., ‘Validation of cytoplasmic-to-nuclear ratio of survivin as an indicator of improved prognosis in breast cancer’, BMC Cancer, vol. 10, no. 1, p. 639, Dec. 2010, doi: 10.1186/1471-2407-10-639.

[21] A.-A. Nahid and Y. Kong, ‘Involvement of Machine Learning for Breast Cancer Image Classification: A Survey’, Comput. Math. Methods Med., vol. 2017, 2017, doi: 10.1155/2017/3781951.

[22] H. Huang et al., ‘A new fruit fly optimization algorithm enhanced support vector machine for diagnosis of breast cancer based on high-level features’, BMC Bioinformatics, vol. 20, 2019, doi: 10.1186/s12859-019-2771-z.

[23] P. Kaur, G. Singh, and P. Kaur, ‘Intellectual detection and validation of automated mammogram breast cancer images by multi-class SVM using deep learning classification’, Inform. Med. Unlocked, vol. 16, 2019, doi: 10.1016/j.imu.2019.01.001.

[24] Y. Min, X. Wei, H. Chen, K. Xiang, G. Yin, and Y. Feng, ‘Identifying Clinicopathological Risk Factors of the Regional Lymph Node Metastasis in Patients with T1-2Mucinous Breast Cancer: A Population-Based Study’, J. Oncol., vol. 2021, 2021, doi: 10.1155/2021/3866907.

[25] X. Lan et al., ‘Application of machine learning with multiparametric dual-energy computed tomography of the breast to differentiate between benign and malignant lesions’, Quant. Imaging Med. Surg., vol. 12, no. 1, pp. 810–822, 2022, doi: 10.21037/qims-21-39.

[26] R. Fusco, M. Di Marzo, C. Sansone, M. Sansone, and A. Petrillo, ‘Breast DCE-MRI: lesion classification using dynamic and morphological features by means of a multiple classifier system’, Eur. Radiol. Exp., vol. 1, no. 1, 2017, doi: 10.1186/s41747-017-0007-4.

[27] R. Fusco et al., ‘Blood oxygenation level dependent magnetic resonance imaging (Mri), dynamic contrast enhanced mri and diffusion weighted mri for benign and malignant breast cancer discrimination: A preliminary experience’, Cancers, vol. 13, no. 10, 2021, doi: 10.3390/cancers13102421.

[28] R. Fusco et al., ‘Radiomic and artificial intelligence analysis with textural metrics, morphological and dynamic perfusion features extracted by dynamic contrast-enhanced magnetic resonance imaging in the classification of breast lesions’, Appl. Sci. Switz., vol. 11, no. 4, pp. 1–16, 2021, doi: 10.3390/app11041880.

[29] M. Sansone et al., ‘Machine Learning Approaches with Textural Features to Calculate Breast Density on Mammography’, Curr. Oncol., vol. 30, no. 1, pp. 839–853, 2023, doi: 10.3390/curroncol30010064.

[30] Y. Zhang et al., ‘Risk factors for axillary lymph node metastases in clinical stage T1-2N0M0 breast cancer patients’, Med. U. S., vol. 98, no. 40, 2019, doi: 10.1097/MD.0000000000017481.

[31] L. Hussain et al., ‘Machine learning classification of texture features of MRI breast tumor and peri-tumor of combined pre- and early treatment predicts pathologic complete response’, Biomed. Eng. Online, vol. 20, no. 1, 2021, doi: 10.1186/s12938-021-00899-z.

[32] K. Wang et al., ‘Integrated multi-omics profiling of high-grade estrogen receptor-positive, HER2-negative breast cancer’, Mol. Oncol., vol. 16, no. 12, pp. 2413–2431, 2022, doi: 10.1002/1878-0261.13043.

[33] Y.-M. Lei et al., ‘Artificial Intelligence in Medical Imaging of the Breast’, Front. Oncol., vol. 11, 2021, doi: 10.3389/fonc.2021.600557.

[34] J. Li et al., ‘Predicting breast cancer 5-year survival using machine learning: A systematic review’, PLoS ONE, vol. 16, no. 4 April, 2021, doi: 10.1371/journal.pone.0250370.

[35] H. Liang, J. Li, H. Wu, L. Li, X. Zhou, and X. Jiang, ‘Mammographic Classification of Breast Cancer Microcalcifications through Extreme Gradient Boosting’, Electron. Switz., vol. 11, no. 15, 2022, doi: 10.3390/electronics11152435.

[36] G. XI et al., ‘Nomogram model combining macro and micro tumor-associated collagen signatures obtained from multiphoton images to predict the histologic grade in breast cancer’, Biomed. Opt. Express, vol. 12, no. 10, pp. 6558–6570, 2021, doi: 10.1364/BOE.433281.

[37] L. Zhao et al., ‘Machine Learning Algorithms Identify Clinical Subtypes and Cancer in Anti-TIF1γ+ Myositis: A Longitudinal Study of 87 Patients’, Front. Immunol., vol. 13, 2022, doi: 10.3389/fimmu.2022.802499.

[38] H. Feng et al., ‘Prediction of radiation-induced acute skin toxicity in breast cancer patients using data encapsulation screening and dose-gradient-based multi-region radiomics technique: A multicenter study’, Front. Oncol., vol. 12, 2022, doi: 10.3389/fonc.2022.1017435.

[39] J. Xu, X. Rao, W. Lu, X. Xie, X. Wang, and X. Li, ‘Noninvasive Predictor for Premalignant and Cancerous Lesions in Endometrial Polyps Diagnosed by Ultrasound’, Front. Oncol., vol. 11, 2022, doi: 10.3389/fonc.2021.812033.

[40] F. Xiong, X. Cao, X. Shi, Z. Long, Y. Liu, and M. Lei, ‘A machine learning–Based model to predict early death among bone metastatic breast cancer patients: A large cohort of 16,189 patients’, Front. Cell Dev. Biol., vol. 10, 2022, doi: 10.3389/fcell.2022.1059597.

[41] F. Su et al., ‘Integrated Tissue and Blood miRNA Expression Profiles Identify Novel Biomarkers for Accurate Non-Invasive Diagnosis of Breast Cancer: Preliminary Results and Future Clinical Implications’, Genes, vol. 13, no. 11, 2022, doi: 10.3390/genes13111931.

[42] D. Gu, W. Zhao, Y. Xie, X. Wang, K. Su, and O. V. Zolotarev, ‘A personalized medical decision support system based on explainable machine learning algorithms and ecc features: Data from the real world’, Diagnostics, vol. 11, no. 9, 2021, doi: 10.3390/diagnostics11091677.

[43] A. C. Kaushik, A. Mehmood, X. Wang, D.-Q. Wei, and X. Dai, ‘Globally ncRNAs Expression Profiling of TNBC and Screening of Functional lncRNA’, Front. Bioeng. Biotechnol., vol. 8, 2021, doi: 10.3389/fbioe.2020.523127.

[44] L. Sun et al., ‘An image segmentation framework for extracting tumors from breast magnetic resonance images’, J. Innov. Opt. Health Sci., vol. 11, no. 4, 2018, doi: 10.1142/S1793545818500141.

[45] S. Smerekanych, T. S. Johnson, K. Huang, and Y. Zhang, ‘Pseudogene-gene functional networks are prognostic of patient survival in breast cancer’, BMC Med. Genomics, vol. 13, 2020, doi: 10.1186/s12920-020-0687-0.

[46] Y. Zhang, Y. Zhou, F. Mao, R. Yao, and Q. Sun, ‘Ki-67 index, progesterone receptor expression, histologic grade and tumor size in predicting breast cancer recurrence risk: A consecutive cohort study’, Cancer Commun., vol. 40, no. 4, pp. 181–193, 2020, doi: 10.1002/cac2.12024.

[47] L. Yang et al., ‘PET/CT-based radiomics analysis may help to predict neoadjuvant chemotherapy outcomes in breast cancer’, Front. Oncol., vol. 12, 2022, doi: 10.3389/fonc.2022.849626.

[48] Y. Peng, W. Li, and Y. Liu, ‘A Hybrid Approach for Biomarker Discovery from Microarray Gene Expression Data for Cancer Classification’, Cancer Inform., vol. 2, p. 117693510600200, Jan. 2006, doi: 10.1177/117693510600200024.

[49] D. Ferraro et al., ‘Microfluidic platform combining droplets and magnetic tweezers: application to HER2 expression in cancer diagnosis’, Sci. Rep., vol. 6, no. 1, p. 25540, May 2016, doi: 10.1038/srep25540.

[50] N. I. Hadi, Q. Jamal, A. Iqbal, F. Shaikh, S. Somroo, and S. G. Musharraf, ‘Serum Metabolomic Profiles for Breast Cancer Diagnosis, Grading and Staging by Gas Chromatography-Mass Spectrometry’, Sci. Rep., vol. 7, no. 1, 2017, doi: 10.1038/s41598-017-01924-9.

[51] D. Sánchez-Calderón, A. Pedraza, C. M. Urrego, A. Mejía-Mejía, A. L. Montealegre-Páez, and S. Perdomo, ‘Analysis of the cost-effectiveness of liquid biopsy to determine treatment change in patients with her2-positive advanced breast cancer in Colombia’, Clin. Outcomes Res., vol. 12, pp. 115–122, 2020, doi: 10.2147/CEOR.S220726.

[52] S. Abbas et al., ‘BCD-WERT: a novel approach for breast cancer detection using whale optimization based efficient features and extremely randomized tree algorithm’, PeerJ Comput. Sci., vol. 7, p. e390, Mar. 2021, doi: 10.7717/peerj-cs.390.

[53] M. Saarela and S. Jauhiainen, ‘Comparison of feature importance measures as explanations for classification models’, SN Appl. Sci., vol. 3, no. 2, 2021, doi: 10.1007/s42452-021-04148-9.

[54] A. H. Osman and H. M. A. Aljahdali, ‘An Effective of Ensemble Boosting Learning Method for Breast Cancer Virtual Screening Using Neural Network Model’, IEEE Access, vol. 8, pp. 39165–39174, 2020, doi: 10.1109/ACCESS.2020.2976149.

[55] M. Kaya Keleş, ‘Breast cancer prediction and detection using data mining classification algorithms: A comparative study’, Teh. Vjesn., vol. 26, no. 1, pp. 149–155, 2019, doi: 10.17559/TV-20180417102943.

[56] C. L. Chowdhary, M. Mittal, P. Kumaresan, P. A. Pattanaik, and Z. Marszalek, ‘INVOLVEMENT OF MACHINE LEARNING TOOLS IN HEALTHCARE DECISION MAKING’, Sens. Switz., vol. 20, no. 14, pp. 1–20, 2020, doi: 10.3390/s20143903.

[57] S. M. D. A. C. Jayatilake and G. U. Ganegoda, ‘Involvement of Machine Learning Tools in Healthcare Decision Making’, J. Healthc. Eng., vol. 2021, 2021, doi: 10.1155/2021/6679512.

[58] N. Al-Azzam and I. Shatnawi, ‘Comparing supervised and semi-supervised Machine Learning Models on Diagnosing Breast Cancer’, Ann. Med. Surg., vol. 62, pp. 53–64, 2021, doi: 10.1016/j.amsu.2020.12.043.

[59] M. Shanbehzadeh, H. Kazemi-Arpanahi, M. Bolbolian Ghalibaf, and A. Orooji, ‘Performance evaluation of machine learning for breast cancer diagnosis: A case study’, Inform. Med. Unlocked, vol. 31, 2022, doi: 10.1016/j.imu.2022.101009.

[60] R. Massafra et al., ‘A Clinical Decision Support System for Predicting Invasive Breast Cancer Recurrence: Preliminary Results’, Front. Oncol., vol. 11, 2021, doi: 10.3389/fonc.2021.576007.

[61] Y. K. Qawqzeh, A. Alourani, and S. Ghwanmeh, ‘An Improved Breast Cancer Classification Method Using an Enhanced AdaBoost Classifier’, Int. J. Adv. Comput. Sci. Appl., vol. 14, no. 1, pp. 473–478, 2023, doi: 10.14569/IJACSA.2023.0140151.

[62] M. N. Nik Ab Kadir et al., ‘Development of Predictive Models for Survival among Women with Breast Cancer in Malaysia’, Int. J. Environ. Res. Public. Health, vol. 19, no. 22, 2022, doi: 10.3390/ijerph192215335.

[63] I. Ozcan, H. Aydin, and A. Cetinkaya, ‘Comparison of Classification Success Rates of Different Machine Learning Algorithms in the Diagnosis of Breast Cancer’, Asian Pac. J. Cancer Prev., vol. 23, no. 10, pp. 3287–3297, 2022, doi: 10.31557/APJCP.2022.23.10.3287.

[64] G. Huang, Y. Yang, Y. Lei, and J. Yang, ‘Differences in Subjective Well-Being between Formal and Informal Workers in Urban China’, Int. J. Environ. Res. Public. Health, vol. 20, no. 1, p. 149, Dec. 2022, doi: 10.3390/ijerph20010149.

[65] M. B. S. Khan, Atta-Ur-Rahman, M. S. Nawaz, R. Ahmed, M. A. Khan, and A. Mosavi, ‘Intelligent breast cancer diagnostic system empowered by deep extreme gradient descent optimization’, Math. Biosci. Eng., vol. 19, no. 8, pp. 7978–8002, 2022, doi: 10.3934/mbe.2022373.




DOI: https://doi.org/10.18860/jocdas.v1i1.25047

DOI (PDF): https://doi.org/10.18860/jocdas.v1i1.25047.g10985

Refbacks

  • There are currently no refbacks.