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Classification of Potential Biomarkers in Breast Cancer Using Artificial Intelligence Algorithms and Anthropometric Datasets
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mt-3 mb-3"> <h5 class="card-header" style="font-size:.9rem">Classification of Potential Biomarkers in Breast Cancer Using Artificial Intelligence Algorithms and Anthropometric Datasets</h5> <div class="card-body"> <p class="card-text"><strong>Authors:</strong> <a href="https://publications.waset.org/search?q=Aref%20Aasi">Aref Aasi</a>, <a href="https://publications.waset.org/search?q=Sahar%20Ebrahimi%20Bajgani"> Sahar Ebrahimi Bajgani</a>, <a href="https://publications.waset.org/search?q=Erfan%20Aasi"> Erfan Aasi</a> </p> <p class="card-text"><strong>Abstract:</strong></p> <p>Breast cancer (BC) continues to be the most frequent cancer in females and causes the highest number of cancer-related deaths in women worldwide. Inspired by recent advances in studying the relationship between different patient attributes and features and the disease, in this paper, we have tried to investigate the different classification methods for better diagnosis of BC in the early stages. In this regard, datasets from the University Hospital Centre of Coimbra were chosen, and different machine learning (ML)-based and neural network (NN) classifiers have been studied. For this purpose, we have selected favorable features among the nine provided attributes from the clinical dataset by using a random forest algorithm. This dataset consists of both healthy controls and BC patients, and it was noted that glucose, BMI, resistin, and age have the most importance, respectively. Moreover, we have analyzed these features with various ML-based classifier methods, including Decision Tree (DT), K-Nearest Neighbors (KNN), eXtreme Gradient Boosting (XGBoost), Logistic Regression (LR), Naive Bayes (NB), and Support Vector Machine (SVM) along with NN-based Multi-Layer Perceptron (MLP) classifier. The results revealed that among different techniques, the SVM and MLP classifiers have the most accuracy, with amounts of 96% and 92%, respectively. These results divulged that the adopted procedure could be used effectively for the classification of cancer cells, and also it encourages further experimental investigations with more collected data for other types of cancers.</p> <iframe src="https://publications.waset.org/10013232.pdf" style="width:100%; height:400px;" frameborder="0"></iframe> <p class="card-text"><strong>Keywords:</strong> <a href="https://publications.waset.org/search?q=Breast%20cancer" title="Breast cancer">Breast cancer</a>, <a href="https://publications.waset.org/search?q=health%20diagnosis" title=" health diagnosis"> health diagnosis</a>, <a href="https://publications.waset.org/search?q=Machine%20Learning" title=" Machine Learning"> Machine Learning</a>, <a href="https://publications.waset.org/search?q=biomarker%20classification" title=" biomarker classification"> biomarker classification</a>, <a href="https://publications.waset.org/search?q=Neural%20Network." title=" Neural Network."> Neural Network.</a> </p> <a href="https://publications.waset.org/10013232/classification-of-potential-biomarkers-in-breast-cancer-using-artificial-intelligence-algorithms-and-anthropometric-datasets" class="btn btn-primary btn-sm">Procedia</a> <a href="https://publications.waset.org/10013232/apa" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">APA</a> <a href="https://publications.waset.org/10013232/bibtex" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">BibTeX</a> <a href="https://publications.waset.org/10013232/chicago" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Chicago</a> <a href="https://publications.waset.org/10013232/endnote" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">EndNote</a> <a href="https://publications.waset.org/10013232/harvard" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">Harvard</a> <a href="https://publications.waset.org/10013232/json" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">JSON</a> <a href="https://publications.waset.org/10013232/mla" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">MLA</a> <a href="https://publications.waset.org/10013232/ris" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">RIS</a> <a href="https://publications.waset.org/10013232/xml" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">XML</a> <a href="https://publications.waset.org/10013232/iso690" target="_blank" rel="nofollow" class="btn btn-primary btn-sm">ISO 690</a> <a href="https://publications.waset.org/10013232.pdf" target="_blank" class="btn btn-primary btn-sm">PDF</a> <span class="bg-info text-light px-1 py-1 float-right rounded"> Downloads <span class="badge badge-light">320</span> </span> <p class="card-text"><strong>References:</strong></p> <br>[1] J. M. Jerez-Aragonés, J. A. Gómez-Ruiz, G. Ramos-Jiménez, J. Muñoz-Pérez, and E. Alba-Conejo, "A combined neural network and decision trees model for prognosis of breast cancer relapse," Artificial intelligence in medicine, vol. 27, no. 1, pp. 45-63, 2003. <br>[2] L. A. Torre, R. L. Siegel, E. M. Ward, and A. Jemal, "Global cancer incidence and mortality rates and trends—an update," Cancer Epidemiology and Prevention Biomarkers, vol. 25, no. 1, pp. 16-27, 2016. <br>[3] C.-W. Chou, Y.-M. Huang, Y.-J. Chang, C.-Y. Huang, and C.-S. Hung, "Identified the novel resistant biomarkers for taxane-based therapy for triple-negative breast cancer," International journal of medical sciences, vol. 18, no. 12, p. 2521, 2021. <br>[4] R. Roslidar et al., "A review on recent progress in thermal imaging and deep learning approaches for breast cancer detection," IEEE Access, vol. 8, pp. 116176-116194, 2020. <br>[5] A. V. Berumen, G. J. Moyao, N. M. Rodriguez, A. M. Ilbawi, A. Migliore, and L. N. Shulman, "Defining priority medical devices for cancer management: a WHO initiative," The Lancet Oncology, vol. 19, no. 12, pp. e709-e719, 2018. <br>[6] O. Ginsburg et al., "Breast cancer early detection: A phased approach to implementation," Cancer, vol. 126, pp. 2379-2393, 2020. <br>[7] M. d. F. O. Baffa and L. G. Lattari, "Convolutional neural networks for static and dynamic breast infrared imaging classification," in 2018 31st SIBGRAPI Conference on Graphics, Patterns and Images (SIBGRAPI), 2018: IEEE, pp. 174-181. <br>[8] H.-J. Chiu, T.-H. S. Li, and P.-H. Kuo, "Breast cancer–detection system using PCA, multi-layer perceptron, transfer learning, and support vector machine," IEEE Access, vol. 8, pp. 204309-204324, 2020. <br>[9] S. Y. Siddiqui et al., "IoMT cloud-based intelligent prediction of breast cancer stages empowered with deep learning," IEEE Access, vol. 9, pp. 146478-146491, 2021. <br>[10] K. Kerlikowske, D. Grady, S. M. Rubin, C. Sandrock, and V. L. Ernster, "Efficacy of screening mammography: a meta-analysis," Jama, vol. 273, no. 2, pp. 149-154, 1995. <br>[11] R. Greenberg, Y. Skornick, and O. Kaplan, "Management of breast fibroadenomas," Journal of general internal medicine, vol. 13, no. 9, pp. 640-645, 1998. <br>[12] P. V. de Campos Souza, Y.-K. Wang, and E. Lughofer, "Knowledge extraction about patients surviving breast cancer treatment through an autonomous fuzzy neural network," in 2020 IEEE International Conference on Fuzzy Systems (FUZZ-IEEE), 2020: IEEE, pp. 1-8. <br>[13] H. Pham and D. H. Pham, "A novel generalized logistic dependent model to predict the presence of breast cancer based on biomarkers," Concurrency and Computation: Practice and Experience, vol. 32, no. 1, p. e5467, 2020. <br>[14] A. Aasi, S. E. Bajgani, and B. Panchapakesan, " A first-principles investigation on the adsorption of octanal and nonanal molecules with decorated monolayer WS2 as promising gas sensing platform," AIP Advances, vol. 13, no. 2, p. 025157, 2023. <br>[15] A. Aasi, E. Aasi, S. Mehdi Aghaei, and B. Panchapakesan, "Green Phosphorene as a Promising Biosensor for Detection of Furan and p-Xylene as Biomarkers of Disease: A DFT Study," Sensors, vol. 22, no. 9, p. 3178, 2022. <br>[16] A. Aasi, S. Mehdi Aghaei, and B. Panchapakesan, "Noble Metal (Pt or Pd)-Decorated Atomically Thin MoS2 as a Promising Material for Sensing Colorectal Cancer Biomarkers Through Exhaled Breath," International Journal of Computational Materials Science and Engineering, p. 2350014, 2023, doi: https://doi.org/ 10.1142/S2047684123500148 <br>[17] R. T. Chlebowski et al., "Predicting risk of breast cancer in postmenopausal women by hormone receptor status," JNCI: Journal of the National Cancer Institute, vol. 99, no. 22, pp. 1695-1705, 2007. <br>[18] A. W. Opstal-van Winden et al., "A bead-based multiplexed immunoassay to evaluate breast cancer biomarkers for early detection in pre-diagnostic serum," International journal of molecular sciences, vol. 13, no. 10, pp. 13587-13604, 2012. <br>[19] K. D. Cole, H. J. He, and L. Wang, "Breast cancer biomarker measurements and standards," PROTEOMICS–Clinical Applications, vol. 7, no. 1-2, pp. 17-29, 2013. <br>[20] J. G. Santillán‐Benítez et al., "The tetrad BMI, leptin, leptin/adiponectin (L/a) ratio and CA 15‐3 are reliable biomarkers of breast cancer," Journal of clinical laboratory analysis, vol. 27, no. 1, pp. 12-20, 2013. <br>[21] M. Dalamaga, G. Sotiropoulos, K. Karmaniolas, N. Pelekanos, E. Papadavid, and A. Lekka, "Serum resistin: a biomarker of breast cancer in postmenopausal women? Association with clinicopathological characteristics, tumor markers, inflammatory and metabolic parameters," Clinical biochemistry, vol. 46, no. 7-8, pp. 584-590, 2013. <br>[22] G. Khakpour, A. Pooladi, P. Izadi, M. Noruzinia, and J. Tavakkoly Bazzaz, "DNA methylation as a promising landscape: A simple blood test for breast cancer prediction," Tumor Biology, vol. 36, no. 7, pp. 4905-4912, 2015. <br>[23] J. Crisostomo et al., "Hyperresistinemia and metabolic dysregulation: a risky crosstalk in obese breast cancer," Endocrine, vol. 53, no. 2, pp. 433-442, 2016. <br>[24] A. Assiri, H. F. Kamel, and M. F. Hassanien, "Resistin, visfatin, adiponectin, and leptin: risk of breast cancer in pre-and postmenopausal Saudi females and their possible diagnostic and predictive implications as novel biomarkers," Disease markers, vol. 2015, 2015. <br>[25] J.-H. Kang, B.-Y. Yu, and D.-S. Youn, "Relationship of serum adiponectin and resistin levels with breast cancer risk," Journal of Korean medical science, vol. 22, no. 1, pp. 117-121, 2007. <br>[26] H. Kobeissi, S. Mohammadzadeh, and E. Lejeune, "Enhancing mechanical metamodels with a generative model-based augmented training dataset," Journal of Biomechanical Engineering, vol. 144, no. 12, p. 121002, 2022. <br>[27] S. Mohammadzadeh and E. Lejeune, "Predicting mechanically driven full-field quantities of interest with deep learning-based metamodels," Extreme Mechanics Letters, vol. 50, p. 101566, 2022. <br>[28] H. Pham and D. H. Pham, "A Median-Based Machine-Learning Approach for Predicting Random Sampling Bernoulli Distribution Parameter," Vietnam Journal of Computer Science, vol. 6, no. 01, pp. 17-28, 2019. <br>[29] E. Aličković and A. Subasi, "Breast cancer diagnosis using GA feature selection and Rotation Forest," Neural Computing and applications, vol. 28, no. 4, pp. 753-763, 2017. <br>[30] M. M. Islam, H. Iqbal, M. R. Haque, and M. K. Hasan, "Prediction of breast cancer using support vector machine and K-Nearest neighbors," in 2017 IEEE Region 10 Humanitarian Technology Conference (R10-HTC), 2017: IEEE, pp. 226-229. <br>[31] N. Khuriwal and N. Mishra, "Breast cancer diagnosis using deep learning algorithm," in 2018 International Conference on Advances in Computing, Communication Control and Networking (ICACCCN), 2018: IEEE, pp. 98-103. <br>[32] L. Liu, "Research on logistic regression algorithm of breast cancer diagnose data by machine learning," in 2018 International Conference on Robots & Intelligent System (ICRIS), 2018: IEEE, pp. 157-160. <br>[33] Q. Wuniri, W. Huangfu, Y. Liu, X. Lin, L. Liu, and Z. Yu, "A generic-driven wrapper embedded with feature-type-aware hybrid Bayesian classifier for breast cancer classification," IEEE Access, vol. 7, pp. 119931-119942, 2019. <br>[34] P. Ghosh, S. Azam, K. M. Hasib, A. Karim, M. Jonkman, and A. Anwar, "A performance based study on deep learning algorithms in the effective prediction of breast cancer," in 2021 International Joint Conference on Neural Networks (IJCNN), 2021: IEEE, pp. 1-8. <br>[35] M. Patricio, J. Pereira, J. Crisostom, P. Matafome, R. Seiça, and F. Cramelo, "Breast Cancer Coimbra Data Set," Web site: https://archive.ics.uci.edu/ml/datasets/Breast+ Cancer+ Coimbra, 2018. <br>[36] V. J. Silva Araújo, A. J. Guimarães, P. V. de Campos Souza, T. S. Rezende, and V. S. Araújo, "Using resistin, glucose, age and BMI and pruning fuzzy neural network for the construction of expert systems in the prediction of breast cancer," Machine Learning and Knowledge Extraction, vol. 1, no. 1, pp. 466-482, 2019. <br>[37] T. S. Furey, N. Cristianini, N. Duffy, D. W. Bednarski, M. Schummer, and D. Haussler, "Support vector machine classification and validation of cancer tissue samples using microarray expression data," Bioinformatics, vol. 16, no. 10, pp. 906-914, 2000. <br>[38] R. Molania et al., "Removing unwanted variation from large-scale RNA sequencing data with PRPS," Nature Biotechnology, pp. 1-14, 2022. <br>[39] R. Bharti, A. Khamparia, M. Shabaz, G. Dhiman, S. Pande, and P. Singh, "Prediction of heart disease using a combination of machine learning and deep learning," Computational intelligence and neuroscience, vol. 2021, 2021. </div> </div> </div> </main> <footer> <div id="infolinks" class="pt-3 pb-2"> <div class="container"> <div style="background-color:#f5f5f5;" class="p-3"> <div class="row"> <div class="col-md-2"> <ul class="list-unstyled"> About <li><a href="https://waset.org/page/support">About Us</a></li> <li><a href="https://waset.org/page/support#legal-information">Legal</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/WASET-16th-foundational-anniversary.pdf">WASET celebrates its 16th foundational anniversary</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Account <li><a href="https://waset.org/profile">My Account</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Explore <li><a href="https://waset.org/disciplines">Disciplines</a></li> <li><a href="https://waset.org/conferences">Conferences</a></li> <li><a href="https://waset.org/conference-programs">Conference Program</a></li> <li><a href="https://waset.org/committees">Committees</a></li> <li><a href="https://publications.waset.org">Publications</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Research <li><a href="https://publications.waset.org/abstracts">Abstracts</a></li> <li><a href="https://publications.waset.org">Periodicals</a></li> <li><a href="https://publications.waset.org/archive">Archive</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Open Science <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Philosophy.pdf">Open Science Philosophy</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Science-Award.pdf">Open Science Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Open-Society-Open-Science-and-Open-Innovation.pdf">Open Innovation</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Postdoctoral-Fellowship-Award.pdf">Postdoctoral Fellowship Award</a></li> <li><a target="_blank" rel="nofollow" href="https://publications.waset.org/static/files/Scholarly-Research-Review.pdf">Scholarly Research Review</a></li> </ul> </div> <div class="col-md-2"> <ul class="list-unstyled"> Support <li><a href="https://waset.org/page/support">Support</a></li> <li><a href="https://waset.org/profile/messages/create">Contact Us</a></li> <li><a href="https://waset.org/profile/messages/create">Report Abuse</a></li> </ul> </div> </div> </div> </div> </div> <div class="container text-center"> <hr style="margin-top:0;margin-bottom:.3rem;"> <a href="https://creativecommons.org/licenses/by/4.0/" target="_blank" class="text-muted small">Creative Commons Attribution 4.0 International License</a> <div id="copy" class="mt-2">© 2024 World Academy of Science, Engineering and Technology</div> </div> </footer> <a href="javascript:" id="return-to-top"><i class="fas fa-arrow-up"></i></a> <div class="modal" id="modal-template"> <div class="modal-dialog"> <div class="modal-content"> <div class="row m-0 mt-1"> <div class="col-md-12"> <button type="button" class="close" data-dismiss="modal" aria-label="Close"><span aria-hidden="true">×</span></button> </div> </div> <div class="modal-body"></div> </div> </div> </div> <script src="https://cdn.waset.org/static/plugins/jquery-3.3.1.min.js"></script> <script src="https://cdn.waset.org/static/plugins/bootstrap-4.2.1/js/bootstrap.bundle.min.js"></script> <script src="https://cdn.waset.org/static/js/site.js?v=150220211556"></script> <script> jQuery(document).ready(function() { /*jQuery.get("https://publications.waset.org/xhr/user-menu", function (response) { jQuery('#mainNavMenu').append(response); });*/ jQuery.get({ url: "https://publications.waset.org/xhr/user-menu", cache: false }).then(function(response){ jQuery('#mainNavMenu').append(response); }); }); </script> </body> </html>