Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence
Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can...
- Autores:
-
Silva, Jesús
Pineda Lezama, Omar Bonerge
Varela, Noel
Adriana Borrero, Luz
- Tipo de recurso:
- http://purl.org/coar/resource_type/c_816b
- Fecha de publicación:
- 2019
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/5134
- Acceso en línea:
- https://hdl.handle.net/11323/5134
https://repositorio.cuc.edu.co/
- Palabra clave:
- Breast cancer
Recurrence events
Nonrecurrence events
K-means clustering
- Rights
- openAccess
- License
- CC0 1.0 Universal
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Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| title |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| spellingShingle |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence Breast cancer Recurrence events Nonrecurrence events K-means clustering |
| title_short |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| title_full |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| title_fullStr |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| title_full_unstemmed |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| title_sort |
Integration of Data Mining Classification Techniques and Ensemble Learning for Predicting the Type of Breast Cancer Recurrence |
| dc.creator.fl_str_mv |
Silva, Jesús Pineda Lezama, Omar Bonerge Varela, Noel Adriana Borrero, Luz |
| dc.contributor.author.spa.fl_str_mv |
Silva, Jesús Pineda Lezama, Omar Bonerge Varela, Noel Adriana Borrero, Luz |
| dc.subject.spa.fl_str_mv |
Breast cancer Recurrence events Nonrecurrence events K-means clustering |
| topic |
Breast cancer Recurrence events Nonrecurrence events K-means clustering |
| description |
Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others. |
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2019 |
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2019-08-08T14:49:05Z |
| dc.date.available.none.fl_str_mv |
2019-08-08T14:49:05Z |
| dc.date.issued.none.fl_str_mv |
2019-04-27 |
| dc.type.spa.fl_str_mv |
Pre-Publicación |
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http://purl.org/coar/resource_type/c_816b |
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Text |
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978-3-030-19222-8 978-3-030-19223-5 |
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https://hdl.handle.net/11323/5134 |
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Corporación Universidad de la Costa |
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REDICUC - Repositorio CUC |
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https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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https://doi.org/10.1007/978-3-030-19223-5_2 |
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1. McPherson, K., Steel, C.M., Dixon, J.M.: ABC of breast diseases: breast cancer-epidemiology, risk factors, and genetics. BMJ 321(7261), 624–628 (2000) CrossRefGoogle Scholar 2. López-Ríos, O., Lazcano-Ponce, E.C., Tovar-Guzman, V., Hernández-Avila, M.: Epidemiology of cancer of the breast in Mexico. Consequences of demography transition. Salud Publica Mex. 39(4), 259–265 (1997) CrossRefGoogle Scholar 3. Romieu, I., Lazcano-Ponce, E., Sanchez-Zamorano, L.M., Willett, W., Hernández-Avila, M.: Carbohydrates and the risk of breast cancer among Mexican women. Cancer Epidemiol. Prev. Biomark. 13(8), 1283–1289 (2004) Google Scholar 4. Rivera-Dommarco, J., Shamah-Levy, T., Villalpando-Hernandez, S., Gonzalez-de Cossio, T., Hernández-Prado, B., Sepulveda, I.: Encuesta Nacional de nutrición 1999. Estado nutricional de niños y mujeres en México. Instituto Nacional de Salud Pública, Cuernavaca (2001) Google Scholar 5. Simpson, J.F., Page, D.L.: Status of breast cancer prognostication based on histopathologic data. Am. J. Clin. Pathol. 102(4 Suppl. 1), S3–S8 (1994) Google Scholar 6. Pereira, H., Pinder, S.E., Sibbering, D.M., Galea, M.H., Elston, C.W., Blamey, R.W., et al.: Pathological prognostic factors in breast cancer. IV: Should you be a typer or a grader? A comparative study of two histological prognostic features in operable breast carcinoma. Histopathology 27(3), 219–226 (1995) CrossRefGoogle Scholar 7. Ellis, I.O., Galea, M., Broughton, N., Locker, A., Blamey, R.W., Elston, C.W.: Pathological prognostic factors in breast cancer. II. Histological type. Relationship with survival in a large study with long-term follow-up. Histopathology 20(6), 479–489 (1992) CrossRefGoogle Scholar 8. Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19(5), 403–410 (1991) CrossRefGoogle Scholar 9. NIH Consensus Conference: Treatment of early-stage breast cancer. JAMA 265(3), 391–395 (1991) CrossRefGoogle Scholar 10. Dabbs, D.J., Silverman, J.F.: Prognostic factors from the fine-needle aspirate: breast carcinoma nuclear grade. Diagn. Cytopathol. 10(3), 203–208 (1994) CrossRefGoogle Scholar 11. Masood, S.: Prognostic factors in breast cancer: use of cytologic preparations. Diagn. Cytopathol. 13(5), 388–395 (1995) MathSciNetCrossRefGoogle Scholar 12. Fisher, E.R., Redmond, C., Fisher, B., Bass, G.: Pathologic findings from the National Surgical Adjuvant Breast and Bowel Projects (NSABP). Prognostic discriminants for 8-year survival for node-negative invasive breast cancer patients. Cancer 65(9 Suppl.), 2121–2128 (1990) CrossRefGoogle Scholar 13. Hortobagyi, G.N., Ames, F.C., Buzdar, A.U., Kau, S.W., McNeese, M.D., Paulus, D., et al.: Management of stage III primary breast cancer with primary chemotherapy, surgery, and radiation therapy. Cancer 62(12), 2507–2516 (1988) CrossRefGoogle Scholar 14. Fan, Q.: Predicting breast cancer recurrence using data mining techniques, pp. 310–311 (2010) Google Scholar 15. Belciug, S., Gorunescu, F., Salem, A.B., Gorunescu, M.: Clustering-based approach for detecting breast cancer recurrence. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 533–538 (2010). https://doi.org/10.1109/ISDA.2010.5687211 16. Swathi, S., Rizwana, S., Babu, G.A.: Classification of neural network structures for breast cancer diagnosis. Int. J. Comput. Sci. Commun. 3(1), 227–231 (2012) Google Scholar 17. Chao, C., Kuo, Y., Cheng, B.: Three artificial intelligence techniques for finding the key factors in breast cancer. J. Stat. Manag. 37–41 (2014). https://doi.org/10.1080/09720510.2012.10701632 18. Park, K., Ali, A., Kim, D., An, Y., Kim, M., Shin, H.: Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 26(9), 2194–2205 (2013). https://doi.org/10.1016/j.engappai.2013.06.013 CrossRefGoogle Scholar 19. Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique (n.d.) Google Scholar 20. Asri, H., Mousannif, H., Al Moatassime, H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83(Fams), 1064–1069 (2016) CrossRefGoogle Scholar 21. Paper, C., Ninkovic, S., Centar, K.: Prediction models for estimation of survival rate and relapse for breast cancer patients (2015/2016) Google Scholar 22. Prghov, F., Prghov, F., Errvwlqj, D.: 527–530 (2017) Google Scholar 23. The UCI (University of California, Irvine): Machine Learning Repository (2019). https://archive.ics.uci.edu/ml/datasets/breast+cancer 24. Viloria, A., Bucci, N., Luna, M.: Comparative analysis between psychosocial risk assessment models. J. Eng. Appl. Sci. 12(11), 2901–2903 (2017). ISSN 1816–949X Google Scholar 25. Caamaño, A.J., Echeverría, M.M., Retamal, V.O., Navarro, C.T., y Espinosa, F.T.: Modelo predictivo de fuga de clientes utilizando minería de datos para una empresa de telecomunicaciones en chile. Universidad Ciencia y Tecnología 18(72), 100–109 (2015) Google Scholar 26. Mark Hall y otros 5 autores: The WEKA data mining software: an update. SIGKDD Explor. 11(1) (2009) Google Scholar 27. Anon, D.: Búsqueda exhaustive. Universidad de Murcia, España (2016). http://dis.um.es/~domingo/apuntes/AlgBio/exhaustiva.pdf 28. Hepner, G.F.: Artificial neural network classification using a minimal training set. Comparison to conventional supervised classification. Photogramm. Eng. Remote Sens. 56(4), 469–473 (1990) Google Scholar 29. Agarwal, B., Mittal, N.: Text classification using machine learning methods - a survey. In: Babu, B.V., et al. (eds.) Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. AISC, vol. 236, pp. 701–709. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1602-5_75 CrossRefGoogle Scholar 30. Larrañaga, P., Inza, I., y Moujahid, A.: Tema 6. Clasificadores Bayesianos. Departamento de Ciencias de la Computación e Inteligencia Artificial (En línea: http://www.sc.ehu.es/ccwbayes/docencia/mmcc/docs/t6bayesianos.pdf. acceso: 9 de enero de 2016), Universidad del País Vasco-Euskal Herriko Unibertsitatea, España (1997) 31. Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Burlington (1993) Google Scholar 32. Kumar, G., Malik, H.: Generalized regression neural network based wind speed prediction model for western region of India. Procedia Comput. Sci. 93(September), 26–32 (2016). https://doi.org/10.1016/j.procs.07.177 CrossRefGoogle Scholar 33. Sun, G., Hoff, S., Zelle, B., Nelson, M.: Development and comparison of backpropagation and generalized regression neural network models to predict diurnal and seasonal gas and PM 10 concentrations and emissions from swine buildings, vol. 0300, no. 08 (2008) Google Scholar 34. Cigizoglu, H.K.: Generalized regression neural network in monthly flow forecasting. Civ. Eng. Environ. Syst. 22(2), 71–84 (2005). https://doi.org/10.1080/10286600500126256 CrossRefGoogle Scholar 35. Kişi, Ö.: Generalized regression neural networks for evapotranspiration modelling generalized regression neural networks for evapotranspiration modelling, 6667 (2010) Google Scholar 36. Kartal, S., Oral, M.: New pattern reduction method for generalized regression neural network. Int. J. Adv. Res. 7(2), 122–129 (2017). https://doi.org/10.23956/ijarcsse/V7I2/01213 CrossRefGoogle Scholar 37. Cross, A.J., Rohrer, G.A., Brown-Brandl, T.M., Cassady, J.P., Keel, B.N.: Feed-forward and generalised regression neural networks in modelling feeding behavior of pigs in the grow-finish phase. Biosyst. Eng. 1–10 (2018). https://doi.org/10.1016/j.biosystemseng.2018.02.005 38. Corso, C.L.: Alternativa de herramienta libre para implementación de aprendizaje automático. http://www.investigacion.frc.utn.edu.ar/labsis/Publicaciones/congresos_labsis/cynthia/Alternativa_de_herramienta_para_Mineria_Datos_CNEISI_2009.pdf. acceso: 10 de agosto de 2015), Argentina (2009) 39. Manickam, R.: Back propagation neural network for prediction of some shell moulding parameters. Period. Polytech. Mech. Eng. 60(4), 203–208 (2016). https://doi.org/10.3311/PPme.8684 MathSciNetCrossRefGoogle Scholar |
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Silva, JesúsPineda Lezama, Omar BonergeVarela, NoelAdriana Borrero, Luz2019-08-08T14:49:05Z2019-08-08T14:49:05Z2019-04-27978-3-030-19222-8978-3-030-19223-5https://hdl.handle.net/11323/5134Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Conservative surgery plus radiotherapy is an alternative to radical mastectomy in the early stages of breast cancer, presenting equivalent survival rates. Data mining facilitates to manage the data and provide the useful medical progression and treatment of cancerous conditions as these methods can help to reduce the number of false positive and false negative decisions. Various machine learning techniques can be used to support the doctors in effective and accurate decision making. In this paper, various classifiers have been tested for the prediction of type of breast cancer recurrence and the results show that neural networks outperform others.Silva, JesúsPineda Lezama, Omar BonergeVarela, NoelAdriana Borrero, LuzengInternational Conference on Green, Pervasive, and Cloud Computinghttps://doi.org/10.1007/978-3-030-19223-5_21. McPherson, K., Steel, C.M., Dixon, J.M.: ABC of breast diseases: breast cancer-epidemiology, risk factors, and genetics. BMJ 321(7261), 624–628 (2000) CrossRefGoogle Scholar 2. López-Ríos, O., Lazcano-Ponce, E.C., Tovar-Guzman, V., Hernández-Avila, M.: Epidemiology of cancer of the breast in Mexico. Consequences of demography transition. Salud Publica Mex. 39(4), 259–265 (1997) CrossRefGoogle Scholar 3. Romieu, I., Lazcano-Ponce, E., Sanchez-Zamorano, L.M., Willett, W., Hernández-Avila, M.: Carbohydrates and the risk of breast cancer among Mexican women. Cancer Epidemiol. Prev. Biomark. 13(8), 1283–1289 (2004) Google Scholar 4. Rivera-Dommarco, J., Shamah-Levy, T., Villalpando-Hernandez, S., Gonzalez-de Cossio, T., Hernández-Prado, B., Sepulveda, I.: Encuesta Nacional de nutrición 1999. Estado nutricional de niños y mujeres en México. Instituto Nacional de Salud Pública, Cuernavaca (2001) Google Scholar 5. Simpson, J.F., Page, D.L.: Status of breast cancer prognostication based on histopathologic data. Am. J. Clin. Pathol. 102(4 Suppl. 1), S3–S8 (1994) Google Scholar 6. Pereira, H., Pinder, S.E., Sibbering, D.M., Galea, M.H., Elston, C.W., Blamey, R.W., et al.: Pathological prognostic factors in breast cancer. IV: Should you be a typer or a grader? A comparative study of two histological prognostic features in operable breast carcinoma. Histopathology 27(3), 219–226 (1995) CrossRefGoogle Scholar 7. Ellis, I.O., Galea, M., Broughton, N., Locker, A., Blamey, R.W., Elston, C.W.: Pathological prognostic factors in breast cancer. II. Histological type. Relationship with survival in a large study with long-term follow-up. Histopathology 20(6), 479–489 (1992) CrossRefGoogle Scholar 8. Elston, C.W., Ellis, I.O.: Pathological prognostic factors in breast cancer. I. The value of histological grade in breast cancer: experience from a large study with long-term follow-up. Histopathology 19(5), 403–410 (1991) CrossRefGoogle Scholar 9. NIH Consensus Conference: Treatment of early-stage breast cancer. JAMA 265(3), 391–395 (1991) CrossRefGoogle Scholar 10. Dabbs, D.J., Silverman, J.F.: Prognostic factors from the fine-needle aspirate: breast carcinoma nuclear grade. Diagn. Cytopathol. 10(3), 203–208 (1994) CrossRefGoogle Scholar 11. Masood, S.: Prognostic factors in breast cancer: use of cytologic preparations. Diagn. Cytopathol. 13(5), 388–395 (1995) MathSciNetCrossRefGoogle Scholar 12. Fisher, E.R., Redmond, C., Fisher, B., Bass, G.: Pathologic findings from the National Surgical Adjuvant Breast and Bowel Projects (NSABP). Prognostic discriminants for 8-year survival for node-negative invasive breast cancer patients. Cancer 65(9 Suppl.), 2121–2128 (1990) CrossRefGoogle Scholar 13. Hortobagyi, G.N., Ames, F.C., Buzdar, A.U., Kau, S.W., McNeese, M.D., Paulus, D., et al.: Management of stage III primary breast cancer with primary chemotherapy, surgery, and radiation therapy. Cancer 62(12), 2507–2516 (1988) CrossRefGoogle Scholar 14. Fan, Q.: Predicting breast cancer recurrence using data mining techniques, pp. 310–311 (2010) Google Scholar 15. Belciug, S., Gorunescu, F., Salem, A.B., Gorunescu, M.: Clustering-based approach for detecting breast cancer recurrence. In: 2010 10th International Conference on Intelligent Systems Design and Applications (ISDA), pp. 533–538 (2010). https://doi.org/10.1109/ISDA.2010.5687211 16. Swathi, S., Rizwana, S., Babu, G.A.: Classification of neural network structures for breast cancer diagnosis. Int. J. Comput. Sci. Commun. 3(1), 227–231 (2012) Google Scholar 17. Chao, C., Kuo, Y., Cheng, B.: Three artificial intelligence techniques for finding the key factors in breast cancer. J. Stat. Manag. 37–41 (2014). https://doi.org/10.1080/09720510.2012.10701632 18. Park, K., Ali, A., Kim, D., An, Y., Kim, M., Shin, H.: Robust predictive model for evaluating breast cancer survivability. Eng. Appl. Artif. Intell. 26(9), 2194–2205 (2013). https://doi.org/10.1016/j.engappai.2013.06.013 CrossRefGoogle Scholar 19. Pritom, A.I., Munshi, M.A.R., Sabab, S.A., Shihab, S.: Predicting breast cancer recurrence using effective classification and feature selection technique (n.d.) Google Scholar 20. Asri, H., Mousannif, H., Al Moatassime, H., Noel, T.: Using machine learning algorithms for breast cancer risk prediction and diagnosis. Procedia Comput. Sci. 83(Fams), 1064–1069 (2016) CrossRefGoogle Scholar 21. Paper, C., Ninkovic, S., Centar, K.: Prediction models for estimation of survival rate and relapse for breast cancer patients (2015/2016) Google Scholar 22. Prghov, F., Prghov, F., Errvwlqj, D.: 527–530 (2017) Google Scholar 23. The UCI (University of California, Irvine): Machine Learning Repository (2019). https://archive.ics.uci.edu/ml/datasets/breast+cancer 24. Viloria, A., Bucci, N., Luna, M.: Comparative analysis between psychosocial risk assessment models. J. Eng. Appl. Sci. 12(11), 2901–2903 (2017). ISSN 1816–949X Google Scholar 25. Caamaño, A.J., Echeverría, M.M., Retamal, V.O., Navarro, C.T., y Espinosa, F.T.: Modelo predictivo de fuga de clientes utilizando minería de datos para una empresa de telecomunicaciones en chile. Universidad Ciencia y Tecnología 18(72), 100–109 (2015) Google Scholar 26. Mark Hall y otros 5 autores: The WEKA data mining software: an update. SIGKDD Explor. 11(1) (2009) Google Scholar 27. Anon, D.: Búsqueda exhaustive. Universidad de Murcia, España (2016). http://dis.um.es/~domingo/apuntes/AlgBio/exhaustiva.pdf 28. Hepner, G.F.: Artificial neural network classification using a minimal training set. Comparison to conventional supervised classification. Photogramm. Eng. Remote Sens. 56(4), 469–473 (1990) Google Scholar 29. Agarwal, B., Mittal, N.: Text classification using machine learning methods - a survey. In: Babu, B.V., et al. (eds.) Proceedings of the Second International Conference on Soft Computing for Problem Solving (SocProS 2012), December 28-30, 2012. AISC, vol. 236, pp. 701–709. Springer, New Delhi (2014). https://doi.org/10.1007/978-81-322-1602-5_75 CrossRefGoogle Scholar 30. Larrañaga, P., Inza, I., y Moujahid, A.: Tema 6. Clasificadores Bayesianos. Departamento de Ciencias de la Computación e Inteligencia Artificial (En línea: http://www.sc.ehu.es/ccwbayes/docencia/mmcc/docs/t6bayesianos.pdf. acceso: 9 de enero de 2016), Universidad del País Vasco-Euskal Herriko Unibertsitatea, España (1997) 31. Quinlan, J.R.: C4. 5: Programs for Machine Learning. Elsevier, Burlington (1993) Google Scholar 32. Kumar, G., Malik, H.: Generalized regression neural network based wind speed prediction model for western region of India. Procedia Comput. Sci. 93(September), 26–32 (2016). https://doi.org/10.1016/j.procs.07.177 CrossRefGoogle Scholar 33. Sun, G., Hoff, S., Zelle, B., Nelson, M.: Development and comparison of backpropagation and generalized regression neural network models to predict diurnal and seasonal gas and PM 10 concentrations and emissions from swine buildings, vol. 0300, no. 08 (2008) Google Scholar 34. Cigizoglu, H.K.: Generalized regression neural network in monthly flow forecasting. Civ. Eng. Environ. Syst. 22(2), 71–84 (2005). https://doi.org/10.1080/10286600500126256 CrossRefGoogle Scholar 35. Kişi, Ö.: Generalized regression neural networks for evapotranspiration modelling generalized regression neural networks for evapotranspiration modelling, 6667 (2010) Google Scholar 36. Kartal, S., Oral, M.: New pattern reduction method for generalized regression neural network. Int. J. Adv. Res. 7(2), 122–129 (2017). https://doi.org/10.23956/ijarcsse/V7I2/01213 CrossRefGoogle Scholar 37. Cross, A.J., Rohrer, G.A., Brown-Brandl, T.M., Cassady, J.P., Keel, B.N.: Feed-forward and generalised regression neural networks in modelling feeding behavior of pigs in the grow-finish phase. Biosyst. Eng. 1–10 (2018). https://doi.org/10.1016/j.biosystemseng.2018.02.005 38. Corso, C.L.: Alternativa de herramienta libre para implementación de aprendizaje automático. http://www.investigacion.frc.utn.edu.ar/labsis/Publicaciones/congresos_labsis/cynthia/Alternativa_de_herramienta_para_Mineria_Datos_CNEISI_2009.pdf. acceso: 10 de agosto de 2015), Argentina (2009) 39. Manickam, R.: Back propagation neural network for prediction of some shell moulding parameters. Period. Polytech. Mech. 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