Method based on data mining techniques for breast cancer recurrence analysis

Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role...

Full description

Autores:
Morales Ortega, Roberto
Lozano-Bernal, German
Ariza Colpas, Paola Patricia
ARRIETA RODRIGUEZ, EUGENIA LUZ
Ospino Mendoza, Elisa
caicedo ortiz, jose antonio
Piñeres-Melo, Marlon Alberto
Mendoza Palechor, Fabio
Roca-Vides, Margarita
Tipo de recurso:
Article of journal
Fecha de publicación:
2020
Institución:
Corporación Universidad de la Costa
Repositorio:
REDICUC - Repositorio CUC
Idioma:
eng
OAI Identifier:
oai:repositorio.cuc.edu.co:11323/7204
Acceso en línea:
https://hdl.handle.net/11323/7204
https://doi.org/10.1007/978-3-030-53956-6_54
https://repositorio.cuc.edu.co/
Palabra clave:
Breast cancer
Data mining
Classification
Cluster
Dataset
Rights
openAccess
License
CC0 1.0 Universal
id RCUC2_df0a6331ec905d20cbcc322dac66f394
oai_identifier_str oai:repositorio.cuc.edu.co:11323/7204
network_acronym_str RCUC2
network_name_str REDICUC - Repositorio CUC
repository_id_str
dc.title.spa.fl_str_mv Method based on data mining techniques for breast cancer recurrence analysis
title Method based on data mining techniques for breast cancer recurrence analysis
spellingShingle Method based on data mining techniques for breast cancer recurrence analysis
Breast cancer
Data mining
Classification
Cluster
Dataset
title_short Method based on data mining techniques for breast cancer recurrence analysis
title_full Method based on data mining techniques for breast cancer recurrence analysis
title_fullStr Method based on data mining techniques for breast cancer recurrence analysis
title_full_unstemmed Method based on data mining techniques for breast cancer recurrence analysis
title_sort Method based on data mining techniques for breast cancer recurrence analysis
dc.creator.fl_str_mv Morales Ortega, Roberto
Lozano-Bernal, German
Ariza Colpas, Paola Patricia
ARRIETA RODRIGUEZ, EUGENIA LUZ
Ospino Mendoza, Elisa
caicedo ortiz, jose antonio
Piñeres-Melo, Marlon Alberto
Mendoza Palechor, Fabio
Roca-Vides, Margarita
dc.contributor.author.spa.fl_str_mv Morales Ortega, Roberto
Lozano-Bernal, German
Ariza Colpas, Paola Patricia
ARRIETA RODRIGUEZ, EUGENIA LUZ
Ospino Mendoza, Elisa
caicedo ortiz, jose antonio
Piñeres-Melo, Marlon Alberto
Mendoza Palechor, Fabio
Roca-Vides, Margarita
dc.subject.spa.fl_str_mv Breast cancer
Data mining
Classification
Cluster
Dataset
topic Breast cancer
Data mining
Classification
Cluster
Dataset
description Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery.
publishDate 2020
dc.date.accessioned.none.fl_str_mv 2020-11-04T20:04:56Z
dc.date.available.none.fl_str_mv 2020-11-04T20:04:56Z
dc.date.issued.none.fl_str_mv 2020
dc.type.spa.fl_str_mv Artículo de revista
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dc.identifier.instname.spa.fl_str_mv Corporación Universidad de la Costa
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dc.relation.references.spa.fl_str_mv 1. Facts and figures of cancer. https://www.who.int/cancer/about/facts/es/
2. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015)
3. Gupta, S., Kumar, D., Sharma, A.: Data mining classification techniques applied for breast cancer diagnosis and prognosis. Ind. J. Comput. Sci. Eng. (IJCSE) 2(2), 188–195 (2011)
4. Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)
5. Bellaachia, A., Guven, E.: Predicting breast cancer survivability using data mining techniques. Age 58(13), 10–110 (2006)
6. Xiong, X., Kim, Y., Baek, Y., Rhee, D.W., Kim, S.H.: Analysis of breast cancer using data mining & statistical techniques. In: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, pp. 82–87. IEEE (2005)
7. Chou, S.M., Lee, T.S., Shao, Y.E., Chen, I.F.: Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst. Appl. 27(1), 133– 142 (2004)
8. Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36(2), 3465–3469 (2009)
9. Hung, P.D., Hanh, T.D., Diep, V.T.: Breast cancer prediction using spark MLlib and ML packages. In: Proceedings of the 2018 5th International Conference on Bioinformatics Research and Applications, pp. 52–59. ACM (2018)
10. Shadman, T.M., Akash, F.S., Ahmed, M.: Machine learning as an indicator for breast cancer prediction, Doctoral dissertation, BRAC University (2018)
11. Alwidian, J., Hammo, B.H., Obeid, N.: WCBA: weighted classification based on association rules algorithm for breast cancer disease. Appl. Soft Comput. 62, 536–549 (2018)
12. Chaurasia, V., Pal, S., Tiwari, B.B.: Prediction of benign and malignant breast cancer using data mining techniques. J. Algorithms Comput. Technol. 12(2), 119–126 (2018)
13. Agarap, A.F.M.: On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp. 5–9. ACM (2018)
14. Chaurasia, V., Pal, S.: A novel approach for breast cancer detection using data mining techniques (2017)
15. Dubey, A.K., Gupta, U., Jain, S.: Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data. Int. J. Adv. Sci. Eng. Inf. Technol. 8(1), 18–29 (2018)
16. Ojha, U., Goel, S.: A study on prediction of breast cancer recurrence using data mining techniques. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp. 527–530. IEEE (2017)
17. Lichman, M.: UCI machine learning repository, University of California, School of Information and Computer Science, Irvine, CA (2019). http://archive.ics.uci.edu/ml/datasets/breast+ cancer
18. Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 25(3), 265–281 (2002)
19. Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009)
20. Polat, K., Güne¸s, S.: Breast cancer diagnosis using least square support vector machine. Digit. Sig. Proc. 17(4), 694–701 (2007)
21. Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61(3), 399–409 (1997)
22. Das, K., Behera, R.N.: A survey on machine learning: concept, algorithms and applications. Int. J. Innov. Res. Comput. Commun. Eng. 5(2), 1301–1309 (2017)
23. Magerman, D.M.: Statistical decision-tree models for parsing. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, pp. 276–283. Association for Computational Linguistics (1995)
24. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2(Nov), 45–66 (2001)
25. Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)
26. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of the International Conference on Computer Vision (1998)
27. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb0026683
28. Bekele, E., et al.: Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD). In: 2016 IEEE virtual reality (VR), pp 121–130 (2016). https://doi.org/10.1109/vr.2016.7504695
29. Picard, R.W., et al.: Affective learning—a manifesto. BT Technol. J. 22(4), 253–269 (2004). https://doi.org/10.1023/B:BTTJ.0000047603.37042.33
30. Patil, T.R., Sherekar, S.S.: Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int. J. Comput. Sci. Appl. 6(2), 256–261 (2013)
31. O’Reilly, K.M.A., Mclaughlin, A.M., Beckett, W.S., Sime, P.J.: Asbestos-related lung disease. Am. Fam. Phys. 75(5), 683–688 (2007)
32. Peddabachigari, S., Abraham, A., Grosan, G., Thomas, J.: Modeling intrusion detection system using hybrid intelligent systems. J. Netw. Comput. Appl. 30(1), 114–132 (2007)
33. Han, J., Kamber, M.: Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2001)
34. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2001). https://doi.org/10.1007/978-0-387-848 58-7
35. Palechor, F.M., De la Hoz Manotas, A., Colpas, P.A., Ojeda, J.S., Ortega, R.M., Melo, M.P.: Cardiovascular disease analysis using supervised and unsupervised data mining techniques. JSW 12(2), 81–90 (2017)
36. Mendoza-Palechor, F.E., Ariza-Colpas, P.P., Sepulveda-Ojeda, J.A., De-la-Hoz-Manotas, A., Piñeres Melo, M.: Fertility analysis method based on supervised and unsupervised data mining techniques (2016)
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spelling Morales Ortega, RobertoLozano-Bernal, GermanAriza Colpas, Paola PatriciaARRIETA RODRIGUEZ, EUGENIA LUZOspino Mendoza, Elisacaicedo ortiz, jose antonioPiñeres-Melo, Marlon AlbertoMendoza Palechor, FabioRoca-Vides, Margarita2020-11-04T20:04:56Z2020-11-04T20:04:56Z20200302-97431611-3349https://hdl.handle.net/11323/7204https://doi.org/10.1007/978-3-030-53956-6_54Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Cancer is a constantly evolving disease, which affects a large number of people worldwide. Great efforts have been made at the research level for the development of tools based on data mining techniques that allow to detect or prevent breast cancer. The large volumes of data play a fundamental role according to the literature consulted, a great variety of dataset oriented to the analysis of the disease has been generated, in this research the Breast Cancer dataset was used, the purpose of the proposed research is to submit comparison of the J48 and randomforest, NaiveBayes and NaiveBayes Simple, SMO Poli-kernel and SMO RBF-Kernel classification algorithms, integrated with the Simple K-Means cluster algorithm for the generation of a model that allows the successful classification of patients who are or Non-recurring breast cancer after having previously undergone surgery for the treatment of said disease, finally the methods that obtained the best levels were SMO Poly-Kernel + Simple K-Means 98.5% of Precision, 98.5% recall, 98.5% TPRATE and 0.2% FPRATE. The results obtained suggest the possibility of using intelligent computational tools based on data mining methods for the detection of breast cancer recurrence in patients who had previously undergone surgery.Morales Ortega, Roberto-will be generated-orcid-0000-0002-8219-9943-600Lozano-Bernal, GermanAriza Colpas, Paola Patricia-will be generated-orcid-0000-0003-4503-5461-600ARRIETA RODRIGUEZ, EUGENIA LUZ-will be generated-orcid-0000-0001-8151-2654-600Ospino Mendoza, Elisa-will be generated-orcid-0000-0002-8001-6736-600caicedo ortiz, jose antonio-will be generated-orcid-0000-0002-9917-9308-600Piñeres-Melo, Marlon AlbertoMendoza Palechor, Fabio-will be generated-orcid-0000-0002-2755-0841-600Roca-Vides, Margarita-will be generated-orcid-0000-0001-8398-5439-600application/pdfengCorporación Universidad de la CostaCC0 1.0 Universalhttp://creativecommons.org/publicdomain/zero/1.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Computer Sciencehttps://link.springer.com/chapter/10.1007%2F978-3-030-53956-6_54Breast cancerData miningClassificationClusterDatasetMethod based on data mining techniques for breast cancer recurrence analysisArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Facts and figures of cancer. https://www.who.int/cancer/about/facts/es/2. Kourou, K., Exarchos, T.P., Exarchos, K.P., Karamouzis, M.V., Fotiadis, D.I.: Machine learning applications in cancer prognosis and prediction. Comput. Struct. Biotechnol. J. 13, 8–17 (2015)3. Gupta, S., Kumar, D., Sharma, A.: Data mining classification techniques applied for breast cancer diagnosis and prognosis. Ind. J. Comput. Sci. Eng. (IJCSE) 2(2), 188–195 (2011)4. Delen, D., Walker, G., Kadam, A.: Predicting breast cancer survivability: a comparison of three data mining methods. Artif. Intell. Med. 34(2), 113–127 (2005)5. Bellaachia, A., Guven, E.: Predicting breast cancer survivability using data mining techniques. Age 58(13), 10–110 (2006)6. Xiong, X., Kim, Y., Baek, Y., Rhee, D.W., Kim, S.H.: Analysis of breast cancer using data mining & statistical techniques. In: Sixth International Conference on Software Engineering, Artificial Intelligence, Networking and Parallel/Distributed Computing and First ACIS International Workshop on Self-Assembling Wireless Network, pp. 82–87. IEEE (2005)7. Chou, S.M., Lee, T.S., Shao, Y.E., Chen, I.F.: Mining the breast cancer pattern using artificial neural networks and multivariate adaptive regression splines. Expert Syst. Appl. 27(1), 133– 142 (2004)8. Karabatak, M., Ince, M.C.: An expert system for detection of breast cancer based on association rules and neural network. Expert Syst. Appl. 36(2), 3465–3469 (2009)9. Hung, P.D., Hanh, T.D., Diep, V.T.: Breast cancer prediction using spark MLlib and ML packages. In: Proceedings of the 2018 5th International Conference on Bioinformatics Research and Applications, pp. 52–59. ACM (2018)10. Shadman, T.M., Akash, F.S., Ahmed, M.: Machine learning as an indicator for breast cancer prediction, Doctoral dissertation, BRAC University (2018)11. Alwidian, J., Hammo, B.H., Obeid, N.: WCBA: weighted classification based on association rules algorithm for breast cancer disease. Appl. Soft Comput. 62, 536–549 (2018)12. Chaurasia, V., Pal, S., Tiwari, B.B.: Prediction of benign and malignant breast cancer using data mining techniques. J. Algorithms Comput. Technol. 12(2), 119–126 (2018)13. Agarap, A.F.M.: On breast cancer detection: an application of machine learning algorithms on the wisconsin diagnostic dataset. In: Proceedings of the 2nd International Conference on Machine Learning and Soft Computing, pp. 5–9. ACM (2018)14. Chaurasia, V., Pal, S.: A novel approach for breast cancer detection using data mining techniques (2017)15. Dubey, A.K., Gupta, U., Jain, S.: Comparative study of K-means and fuzzy C-means algorithms on the breast cancer data. Int. J. Adv. Sci. Eng. Inf. Technol. 8(1), 18–29 (2018)16. Ojha, U., Goel, S.: A study on prediction of breast cancer recurrence using data mining techniques. In: 2017 7th International Conference on Cloud Computing, Data Science & Engineering-Confluence, pp. 527–530. IEEE (2017)17. Lichman, M.: UCI machine learning repository, University of California, School of Information and Computer Science, Irvine, CA (2019). http://archive.ics.uci.edu/ml/datasets/breast+ cancer18. Abbass, H.A.: An evolutionary artificial neural networks approach for breast cancer diagnosis. Artif. Intell. Med. 25(3), 265–281 (2002)19. Akay, M.F.: Support vector machines combined with feature selection for breast cancer diagnosis. Expert Syst. Appl. 36(2), 3240–3247 (2009)20. Polat, K., Güne¸s, S.: Breast cancer diagnosis using least square support vector machine. Digit. Sig. Proc. 17(4), 694–701 (2007)21. Friedl, M.A., Brodley, C.E.: Decision tree classification of land cover from remotely sensed data. Remote Sens. Environ. 61(3), 399–409 (1997)22. Das, K., Behera, R.N.: A survey on machine learning: concept, algorithms and applications. Int. J. Innov. Res. Comput. Commun. Eng. 5(2), 1301–1309 (2017)23. Magerman, D.M.: Statistical decision-tree models for parsing. In: Proceedings of the 33rd Annual Meeting on Association for Computational Linguistics, pp. 276–283. Association for Computational Linguistics (1995)24. Tong, S., Koller, D.: Support vector machine active learning with applications to text classification. J. Mach. Learn. Res. 2(Nov), 45–66 (2001)25. Vapnik, V.: Statistical Learning Theory. Wiley, Hoboken (1998)26. Papageorgiou, C., Oren, M., Poggio, T.: A general framework for object detection. In: Proceedings of the International Conference on Computer Vision (1998)27. Joachims, T.: Text categorization with support vector machines: learning with many relevant features. In: Nédellec, C., Rouveirol, C. (eds.) ECML 1998. LNCS, vol. 1398, pp. 137–142. Springer, Heidelberg (1998). https://doi.org/10.1007/BFb002668328. Bekele, E., et al.: Multimodal adaptive social interaction in virtual environment (MASI-VR) for children with Autism spectrum disorders (ASD). In: 2016 IEEE virtual reality (VR), pp 121–130 (2016). https://doi.org/10.1109/vr.2016.750469529. Picard, R.W., et al.: Affective learning—a manifesto. BT Technol. J. 22(4), 253–269 (2004). https://doi.org/10.1023/B:BTTJ.0000047603.37042.3330. Patil, T.R., Sherekar, S.S.: Performance analysis of Naive Bayes and J48 classification algorithm for data classification. Int. J. Comput. Sci. Appl. 6(2), 256–261 (2013)31. O’Reilly, K.M.A., Mclaughlin, A.M., Beckett, W.S., Sime, P.J.: Asbestos-related lung disease. Am. Fam. Phys. 75(5), 683–688 (2007)32. Peddabachigari, S., Abraham, A., Grosan, G., Thomas, J.: Modeling intrusion detection system using hybrid intelligent systems. J. Netw. Comput. Appl. 30(1), 114–132 (2007)33. Han, J., Kamber, M.: Data mining: concepts and techniques, 2nd edn. Morgan Kaufmann Publishers, San Francisco (2001)34. Hastie, T., Tibshirani, R., Friedman, J.H.: The Elements of Statistical Learning: Data Mining, Inference and Prediction. Springer, New York (2001). https://doi.org/10.1007/978-0-387-848 58-735. Palechor, F.M., De la Hoz Manotas, A., Colpas, P.A., Ojeda, J.S., Ortega, R.M., Melo, M.P.: Cardiovascular disease analysis using supervised and unsupervised data mining techniques. JSW 12(2), 81–90 (2017)36. Mendoza-Palechor, F.E., Ariza-Colpas, P.P., Sepulveda-Ojeda, J.A., De-la-Hoz-Manotas, A., Piñeres Melo, M.: Fertility analysis method based on supervised and unsupervised data mining techniques (2016)PublicationORIGINALMethod Based on Data Mining Techniques for Breast Cancer Recurrence Analysis.pdfMethod Based on Data Mining Techniques for Breast Cancer Recurrence Analysis.pdfapplication/pdf993998https://repositorio.cuc.edu.co/bitstreams/b2d6312c-eb7b-47a8-a82d-e61f8a8866d5/download51f2179173111cd485d948aef99fd8d6MD51CC-LICENSElicense_rdflicense_rdfapplication/rdf+xml; charset=utf-8701https://repositorio.cuc.edu.co/bitstreams/cfd7057d-8846-4729-9d97-3716c1574a47/download42fd4ad1e89814f5e4a476b409eb708cMD52LICENSElicense.txtlicense.txttext/plain; charset=utf-83196https://repositorio.cuc.edu.co/bitstreams/8ae64b1c-660d-4da0-9763-53f66e5e7fa9/downloade30e9215131d99561d40d6b0abbe9badMD53THUMBNAILMethod Based on Data Mining Techniques for Breast Cancer Recurrence Analysis.pdf.jpgMethod 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