Intelligent fuzzy system to predict the wisconsin breast cancer dataset
Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and...
- Autores:
-
Hernandez, Yamid
Díaz-Pertuz, Leonardo Antonio
Prieto Guevara, Martha Janeth
BARRIOS BARRIOS, MAURICIO ANDRES
Nieto Bernal, Wilson
- Tipo de recurso:
- Article of investigation
- Fecha de publicación:
- 2023
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10457
- Acceso en línea:
- https://hdl.handle.net/11323/10457
https://repositorio.cuc.edu.co/
- Palabra clave:
- Fuzzy system
Breast cancer
Clusters
Pivot tables
- Rights
- openAccess
- License
- Atribución 4.0 Internacional (CC BY 4.0)
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Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| title |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| spellingShingle |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset Fuzzy system Breast cancer Clusters Pivot tables |
| title_short |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| title_full |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| title_fullStr |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| title_full_unstemmed |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| title_sort |
Intelligent fuzzy system to predict the wisconsin breast cancer dataset |
| dc.creator.fl_str_mv |
Hernandez, Yamid Díaz-Pertuz, Leonardo Antonio Prieto Guevara, Martha Janeth BARRIOS BARRIOS, MAURICIO ANDRES Nieto Bernal, Wilson |
| dc.contributor.author.none.fl_str_mv |
Hernandez, Yamid Díaz-Pertuz, Leonardo Antonio Prieto Guevara, Martha Janeth BARRIOS BARRIOS, MAURICIO ANDRES Nieto Bernal, Wilson |
| dc.subject.proposal.eng.fl_str_mv |
Fuzzy system Breast cancer Clusters Pivot tables |
| topic |
Fuzzy system Breast cancer Clusters Pivot tables |
| description |
Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance’ metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems—FIS, demonstrating superior precision. |
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2023 |
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2023-09-07T16:34:31Z |
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2023 |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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Hernández-Julio, Y.F.; Díaz-Pertuz, L.A.; Prieto-Guevara, M.J.; Barrios-Barrios, M.A.; Nieto-Bernal, W. Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset. Int. J. Environ. Res. Public Health 2023, 20, 5103. https://doi.org/10.3390/ ijerph20065103 |
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1661-7827 |
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https://hdl.handle.net/11323/10457 |
| dc.identifier.doi.none.fl_str_mv |
10.3390/ijerph20065103 |
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1660-4601 |
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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/ |
| identifier_str_mv |
Hernández-Julio, Y.F.; Díaz-Pertuz, L.A.; Prieto-Guevara, M.J.; Barrios-Barrios, M.A.; Nieto-Bernal, W. Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset. Int. J. Environ. Res. Public Health 2023, 20, 5103. https://doi.org/10.3390/ ijerph20065103 1661-7827 10.3390/ijerph20065103 1660-4601 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/10457 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
| dc.relation.ispartofjournal.spa.fl_str_mv |
International Journal of Environmental Research and Public Health |
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1. American Cancer Society. Cancer Facts & Figures 2018; American Cancer Society Inc.: Atlanta, GA, USA, 2018; p. 76. 2. Breast Cancer Now. What are the Signs and Symptoms of Breast Cancer? Available online: https://breastcancernow.org/aboutus/media/facts-statistics#signs-and-symptoms (accessed on 3 February 2022). 3. Hayat, M.A. Breast Cancer: An Introduction. In Methods of Cancer Diagnosis, Therapy and Prognosis: Breast Carcinoma; Hayat, M.A., Ed.; Springer Netherlands: Dordrecht, The Netherlands, 2008; pp. 1–3. [CrossRef] 4. Nilashi, M.; Ibrahim, O.; Ahmadi, H.; Shahmoradi, L. A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat. Inform. 2017, 34, 133–144. [CrossRef] 5. Gayathri, B.M.; Sumathi, C.P. Mamdani fuzzy inference system for breast cancer risk detection. In Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 10–12 December 2015; pp. 1–6. 6. Ahmadi, H.; Gholamzadeh, M.; Shahmoradi, L.; Nilashi, M.; Rashvand, P. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Comp. Methods Programs Biomed. 2018, 161, 145–172. [CrossRef] [PubMed] 7. Zadeh, L.A. Fuzzy sets. Inform. Control 1965, 8, 338–353. [CrossRef] 8. Riza, L.S.; Bergmeir, C.N.; Herrera, F.; Benítez Sánchez, J.M. Frbs: Fuzzy rule-based systems for classification and regression in R. J. Stat. Softw. 2015, 65. [CrossRef] 9. Zadeh, L.A. The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 1975, 8, 199–249. [CrossRef] 10. Hamam, A.; Georganas, N.D. A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In Proceedings of the Haptic Audio Visual Environments and Games, Ottawa, ON, Canada, 18–19 October 2008; pp. 87–92. 11. Paul, A.K.; Shill, P.C.; Rabin, M.R.I.; Kundu, A.; Akhand, M.A.H. Fuzzy membership function generation using DMS-PSO for the diagnosis of heart disease. In Proceedings of the 2015 18th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 21–23 December 2015; pp. 456–461. 12. Romero-Córdoba, R.; Olivas, J.Á.; Romero, F.P.; Alonso-Gómez, F. Clinical Decision Support System for the Diagnosis and Treatment of Fuzzy Diseases; Springer: Cham, Switzerland, 2015; pp. 128–138. 13. d’Acierno, A.; Esposito, M.; De Pietro, G. An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications. BMC Bioinform. 2013, 14, S4. [CrossRef] [PubMed] 14. Romero-Córdoba, R.; Olivas, J.A.; Romero, F.P.; Alonso-Gonzalez, F.; Serrano-Guerrero, J. An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases. Int. J. Intell. Syst. 2017, 32, 194–210. [CrossRef] 15. Nazari, S.; Fallah, M.; Kazemipoor, H.; Salehipour, A. A fuzzy inference-fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Syst. Appl. 2018, 95, 261–271. [CrossRef] 16. Hernández-Julio, Y.F.; Prieto-Guevara, M.J.; Nieto-Bernal, W.; Meriño-Fuentes, I.; Guerrero-Avendaño, A. Framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems. Diagnostics 2019, 9, 52. [CrossRef] [PubMed] 17. Hernández-Julio, Y.F.; Nieto-Bernal, W.; Muñoz-Hernández, H. Framework for the Development of Data-Driven Mamdani-Type Fuzzy Decision Support Systems Based on Fuzzy set Theory Using Clusters and Pivot Tables, 1st ed.; Universidad del Sinú Elías Bechara Zainúm: Montería, Colombia, 2021; Volume 1. 18. Bache, K.; Lichman, M. UCI Machine Learning Repository; University of California, School of Information and Computer Science: Irvine, CA, USA, 2013; Available online: https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) (accessed on 1 November 2021). 19. Mangasarian, O.L. Cancer diagnosis via linear programming. SIAM News 1990, 23, 1–18. 20. Onan, A. A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Syst. Appl. 2015, 42, 6844–6852. [CrossRef] 21. Aghabozorgi, S.; Teh, Y.W. Stock market co-movement assessment using a three-phase clustering method. Expert Syst. Appl. 2014, 41, 1301–1314. [CrossRef] 22. Hernández-Julio, Y.F.; Yanagi, T.; de Fátima Ávila Pires, M.; Lopes, M.A.; Ribeiro de Lima, R. Models for Prediction of Physiological Responses of Holstein Dairy Cows. Appl. Art. Intell. 2014, 28, 766–792. [CrossRef] 23. The MathWorks Inc. Unique Values in Array, 2017b; The MathWorks Inc.: Natick, MA, USA, 2017. 24. Tanaka, K. An Introduction to Fuzzy Logic for Practical Applications, 1st ed.; Springer: New York, NY, USA, 1996. 25. Sivanandam, S.; Sumathi, S.; Deepa, S. Introduction to Fuzzy Logic Using MATLAB; Springer: Berlin/Heidelberg, Germany, 2007; Volume 1. 26. de Barros, L.C.; Bassanezi, R.C. Tópicos de Lógica Fuzzy e Biomatemática; Grupo de Biomatemática, Instituto de Matemática, Estatística e Computação Científica (IMECC), Universidade Estadual de Campinas (UNICAMP): Campinas, Brazil, 2010. 27. Alpaydin, E. Introduction to Machine Learning; MIT Press: Cambridge, MA, USA, 2009. 28. Liu, K.; Kang, G.; Zhang, N.; Hou, B. Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks. IEEE Access 2018, 6, 23722–23732. [CrossRef] 29. Zemouri, R.; Omri, N.; Devalland, C.; Arnould, L.; Morello, B.; Zerhouni, N.; Fnaiech, F. Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network. In Proceedings of the 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, 28–30 March 2018; pp. 159–164. 30. Nguyen, T.; Khosravi, A.; Creighton, D.; Nahavandi, S. Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl. Soft Comput. 2015, 30, 812–822. [CrossRef] 31. Hernández-Julio, Y.F.; Hernández, H.M.; Guzmán, J.D.C.; Nieto-Bernal, W.; Díaz, R.R.G.; Ferraz, P.P. Fuzzy Knowledge Discovery and Decision-Making Through Clustering and Dynamic Tables: Application in Medicine. In Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing; Rocha, Á., Ferrás, C., Paredes, M., Eds.; Springer: Cham, Switzerland, 2019; Volume 918, pp. 122–130. 32. Abdel-Zaher, A.M.; Eldeib, A.M. Breast cancer classification using deep belief networks. Expert Syst. Appl. 2016, 46, 139–144. [CrossRef] 33. Thungrut, W.; Wattanapongsakorn, N. Diabetes Classification with Fuzzy Genetic Algorithm. In Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing; Unger, H., Sodsee, S., Meesad, P., Eds.; Springer: Cham, Switzerland, 2018; Volume 769, pp. 107–114. 34. Gorunescu, F. Data Mining: Concepts, Models and Techniques; Springer: Berlin/Heidelberg, Germany, 2011; Volume 12, p. XII. |
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Atribución 4.0 Internacional (CC BY 4.0)© 2023 by the authors. Licensee MDPI, Basel, Switzerland.https://creativecommons.org/licenses/by/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Hernandez, YamidDíaz-Pertuz, Leonardo AntonioPrieto Guevara, Martha JanethBARRIOS BARRIOS, MAURICIO ANDRESNieto Bernal, Wilson2023-09-07T16:34:31Z2023-09-07T16:34:31Z2023Hernández-Julio, Y.F.; Díaz-Pertuz, L.A.; Prieto-Guevara, M.J.; Barrios-Barrios, M.A.; Nieto-Bernal, W. Intelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset. Int. J. Environ. Res. Public Health 2023, 20, 5103. https://doi.org/10.3390/ ijerph200651031661-7827https://hdl.handle.net/11323/1045710.3390/ijerph200651031660-4601Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Decision Support Systems (DSSs) are solutions that serve decision-makers in their decision-making process. For the development of these intelligent systems, two primary components are needed: the knowledge database and the knowledge rule base. The objective of this research work was to implement and validate diverse clinical decision support systems supported by Mamdani-type fuzzy set theory using clustering and dynamic tables. The outcomes were evaluated with other works obtained from the literature to validate the suggested fuzzy systems for categorizing the Wisconsin breast cancer dataset. The fuzzy Inference Systems worked with different input features, according to the studies obtained from the literature. The outcomes confirm that most performance’ metrics in several cases were greater than the achieved results from the literature for the output variable for the different Fuzzy Inference Systems—FIS, demonstrating superior precision.14 páginasapplication/pdfengMultidisciplinary Digital Publishing Institute (MDPI)Switzerlandhttps://www.mdpi.com/1660-4601/20/6/5103Intelligent fuzzy system to predict the wisconsin breast cancer datasetArtículo de revistahttp://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/publishedVersionhttp://purl.org/coar/version/c_970fb48d4fbd8a85International Journal of Environmental Research and Public Health1. American Cancer Society. Cancer Facts & Figures 2018; American Cancer Society Inc.: Atlanta, GA, USA, 2018; p. 76.2. Breast Cancer Now. What are the Signs and Symptoms of Breast Cancer? Available online: https://breastcancernow.org/aboutus/media/facts-statistics#signs-and-symptoms (accessed on 3 February 2022).3. Hayat, M.A. Breast Cancer: An Introduction. In Methods of Cancer Diagnosis, Therapy and Prognosis: Breast Carcinoma; Hayat, M.A., Ed.; Springer Netherlands: Dordrecht, The Netherlands, 2008; pp. 1–3. [CrossRef]4. Nilashi, M.; Ibrahim, O.; Ahmadi, H.; Shahmoradi, L. A knowledge-based system for breast cancer classification using fuzzy logic method. Telemat. Inform. 2017, 34, 133–144. [CrossRef]5. Gayathri, B.M.; Sumathi, C.P. Mamdani fuzzy inference system for breast cancer risk detection. In Proceedings of the 2015 IEEE International Conference on Computational Intelligence and Computing Research (ICCIC), Madurai, India, 10–12 December 2015; pp. 1–6.6. Ahmadi, H.; Gholamzadeh, M.; Shahmoradi, L.; Nilashi, M.; Rashvand, P. Diseases diagnosis using fuzzy logic methods: A systematic and meta-analysis review. Comp. Methods Programs Biomed. 2018, 161, 145–172. [CrossRef] [PubMed]7. Zadeh, L.A. Fuzzy sets. Inform. Control 1965, 8, 338–353. [CrossRef]8. Riza, L.S.; Bergmeir, C.N.; Herrera, F.; Benítez Sánchez, J.M. Frbs: Fuzzy rule-based systems for classification and regression in R. J. Stat. Softw. 2015, 65. [CrossRef]9. Zadeh, L.A. The concept of a linguistic variable and its application to approximate reasoning—I. Inf. Sci. 1975, 8, 199–249. [CrossRef]10. Hamam, A.; Georganas, N.D. A comparison of Mamdani and Sugeno fuzzy inference systems for evaluating the quality of experience of Hapto-Audio-Visual applications. In Proceedings of the Haptic Audio Visual Environments and Games, Ottawa, ON, Canada, 18–19 October 2008; pp. 87–92.11. Paul, A.K.; Shill, P.C.; Rabin, M.R.I.; Kundu, A.; Akhand, M.A.H. Fuzzy membership function generation using DMS-PSO for the diagnosis of heart disease. In Proceedings of the 2015 18th International Conference on Computer and Information Technology (ICCIT), Dhaka, Bangladesh, 21–23 December 2015; pp. 456–461.12. Romero-Córdoba, R.; Olivas, J.Á.; Romero, F.P.; Alonso-Gómez, F. Clinical Decision Support System for the Diagnosis and Treatment of Fuzzy Diseases; Springer: Cham, Switzerland, 2015; pp. 128–138.13. d’Acierno, A.; Esposito, M.; De Pietro, G. An extensible six-step methodology to automatically generate fuzzy DSSs for diagnostic applications. BMC Bioinform. 2013, 14, S4. [CrossRef] [PubMed]14. Romero-Córdoba, R.; Olivas, J.A.; Romero, F.P.; Alonso-Gonzalez, F.; Serrano-Guerrero, J. An Application of Fuzzy Prototypes to the Diagnosis and Treatment of Fuzzy Diseases. Int. J. Intell. Syst. 2017, 32, 194–210. [CrossRef]15. Nazari, S.; Fallah, M.; Kazemipoor, H.; Salehipour, A. A fuzzy inference-fuzzy analytic hierarchy process-based clinical decision support system for diagnosis of heart diseases. Expert Syst. Appl. 2018, 95, 261–271. [CrossRef]16. Hernández-Julio, Y.F.; Prieto-Guevara, M.J.; Nieto-Bernal, W.; Meriño-Fuentes, I.; Guerrero-Avendaño, A. Framework for the development of data-driven Mamdani-type fuzzy clinical decision support systems. Diagnostics 2019, 9, 52. [CrossRef] [PubMed]17. Hernández-Julio, Y.F.; Nieto-Bernal, W.; Muñoz-Hernández, H. Framework for the Development of Data-Driven Mamdani-Type Fuzzy Decision Support Systems Based on Fuzzy set Theory Using Clusters and Pivot Tables, 1st ed.; Universidad del Sinú Elías Bechara Zainúm: Montería, Colombia, 2021; Volume 1.18. Bache, K.; Lichman, M. UCI Machine Learning Repository; University of California, School of Information and Computer Science: Irvine, CA, USA, 2013; Available online: https://archive.ics.uci.edu/ml/datasets/breast+cancer+wisconsin+(original) (accessed on 1 November 2021).19. Mangasarian, O.L. Cancer diagnosis via linear programming. SIAM News 1990, 23, 1–18.20. Onan, A. A fuzzy-rough nearest neighbor classifier combined with consistency-based subset evaluation and instance selection for automated diagnosis of breast cancer. Expert Syst. Appl. 2015, 42, 6844–6852. [CrossRef]21. Aghabozorgi, S.; Teh, Y.W. Stock market co-movement assessment using a three-phase clustering method. Expert Syst. Appl. 2014, 41, 1301–1314. [CrossRef]22. Hernández-Julio, Y.F.; Yanagi, T.; de Fátima Ávila Pires, M.; Lopes, M.A.; Ribeiro de Lima, R. Models for Prediction of Physiological Responses of Holstein Dairy Cows. Appl. Art. Intell. 2014, 28, 766–792. [CrossRef]23. The MathWorks Inc. Unique Values in Array, 2017b; The MathWorks Inc.: Natick, MA, USA, 2017.24. Tanaka, K. An Introduction to Fuzzy Logic for Practical Applications, 1st ed.; Springer: New York, NY, USA, 1996.25. Sivanandam, S.; Sumathi, S.; Deepa, S. Introduction to Fuzzy Logic Using MATLAB; Springer: Berlin/Heidelberg, Germany, 2007; Volume 1.26. de Barros, L.C.; Bassanezi, R.C. Tópicos de Lógica Fuzzy e Biomatemática; Grupo de Biomatemática, Instituto de Matemática, Estatística e Computação Científica (IMECC), Universidade Estadual de Campinas (UNICAMP): Campinas, Brazil, 2010.27. Alpaydin, E. Introduction to Machine Learning; MIT Press: Cambridge, MA, USA, 2009.28. Liu, K.; Kang, G.; Zhang, N.; Hou, B. Breast Cancer Classification Based on Fully-Connected Layer First Convolutional Neural Networks. IEEE Access 2018, 6, 23722–23732. [CrossRef]29. Zemouri, R.; Omri, N.; Devalland, C.; Arnould, L.; Morello, B.; Zerhouni, N.; Fnaiech, F. Breast cancer diagnosis based on joint variable selection and Constructive Deep Neural Network. In Proceedings of the 2018 IEEE 4th Middle East Conference on Biomedical Engineering (MECBME), Tunis, Tunisia, 28–30 March 2018; pp. 159–164.30. Nguyen, T.; Khosravi, A.; Creighton, D.; Nahavandi, S. Medical data classification using interval type-2 fuzzy logic system and wavelets. Appl. Soft Comput. 2015, 30, 812–822. [CrossRef]31. Hernández-Julio, Y.F.; Hernández, H.M.; Guzmán, J.D.C.; Nieto-Bernal, W.; Díaz, R.R.G.; Ferraz, P.P. Fuzzy Knowledge Discovery and Decision-Making Through Clustering and Dynamic Tables: Application in Medicine. In Information Technology and Systems. ICITS 2019. Advances in Intelligent Systems and Computing; Rocha, Á., Ferrás, C., Paredes, M., Eds.; Springer: Cham, Switzerland, 2019; Volume 918, pp. 122–130.32. Abdel-Zaher, A.M.; Eldeib, A.M. Breast cancer classification using deep belief networks. Expert Syst. Appl. 2016, 46, 139–144. [CrossRef]33. Thungrut, W.; Wattanapongsakorn, N. Diabetes Classification with Fuzzy Genetic Algorithm. In Recent Advances in Information and Communication Technology 2018. IC2IT 2018. Advances in Intelligent Systems and Computing; Unger, H., Sodsee, S., Meesad, P., Eds.; Springer: Cham, Switzerland, 2018; Volume 769, pp. 107–114.34. Gorunescu, F. Data Mining: Concepts, Models and Techniques; Springer: Berlin/Heidelberg, Germany, 2011; Volume 12, p. XII.141620Fuzzy systemBreast cancerClustersPivot tablesPublicationORIGINALIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdfIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdfArtículoapplication/pdf1604798https://repositorio.cuc.edu.co/bitstreams/9d624b94-6f5e-4de6-9be5-fe2bf083dfbc/download11a29fe53b377d34ed82140144634216MD51LICENSElicense.txtlicense.txttext/plain; charset=utf-814828https://repositorio.cuc.edu.co/bitstreams/61c4884e-6ae4-439c-a9fd-fdf2e846eaab/download2f9959eaf5b71fae44bbf9ec84150c7aMD52TEXTIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdf.txtIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdf.txtExtracted texttext/plain53487https://repositorio.cuc.edu.co/bitstreams/1df3449c-4d68-4d1b-8625-c11a8525f89b/downloadee28b791a1e7c136a760ee3c0afece90MD53THUMBNAILIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdf.jpgIntelligent Fuzzy System to Predict the Wisconsin Breast Cancer Dataset.pdf.jpgGenerated Thumbnailimage/jpeg15712https://repositorio.cuc.edu.co/bitstreams/2a7ab80e-b0cf-4711-a55c-6f3dd8989546/downloada81072ab168ebc765d7db597eef8ec60MD5411323/10457oai:repositorio.cuc.edu.co:11323/104572024-09-17 10:15:10.582https://creativecommons.org/licenses/by/4.0/© 2023 by the authors. 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ada en las Obras Colectivas.

b.	Distribuir copias o fonogramas de las Obras, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública, incluyéndolas como incorporadas en Obras Colectivas, según corresponda.

c.	Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.
Los derechos mencionados anteriormente pueden ser ejercidos en todos los medios y formatos, actualmente conocidos o que se inventen en el futuro. Los derechos antes mencionados incluyen el derecho a realizar dichas modificaciones en la medida que sean técnicamente necesarias para ejercer los derechos en otro medio o formatos, pero de otra manera usted no está autorizado para realizar obras derivadas. Todos los derechos no otorgados expresamente por el Licenciante quedan por este medio reservados, incluyendo pero sin limitarse a aquellos que se mencionan en las secciones 4(d) y 4(e).

4. Restricciones.
La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:

a.	Usted puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra sólo bajo las condiciones de esta Licencia, y Usted debe incluir una copia de esta licencia o del Identificador Universal de Recursos de la misma con cada copia de la Obra que distribuya, exhiba públicamente, ejecute públicamente o ponga a disposición pública. No es posible ofrecer o imponer ninguna condición sobre la Obra que altere o limite las condiciones de esta Licencia o el ejercicio de los derechos de los destinatarios otorgados en este documento. No es posible sublicenciar la Obra. Usted debe mantener intactos todos los avisos que hagan referencia a esta Licencia y a la cláusula de limitación de garantías. Usted no puede distribuir, exhibir públicamente, ejecutar públicamente, o poner a disposición pública la Obra con alguna medida tecnológica que controle el acceso o la utilización de ella de una forma que sea inconsistente con las condiciones de esta Licencia. Lo anterior se aplica a la Obra incorporada a una Obra Colectiva, pero esto no exige que la Obra Colectiva aparte de la obra misma quede sujeta a las condiciones de esta Licencia. Si Usted crea una Obra Colectiva, previo aviso de cualquier Licenciante debe, en la medida de lo posible, eliminar de la Obra Colectiva cualquier referencia a dicho Licenciante o al Autor Original, según lo solicitado por el Licenciante y conforme lo exige la cláusula 4(c).

b.	Usted no puede ejercer ninguno de los derechos que le han sido otorgados en la Sección 3 precedente de modo que estén principalmente destinados o directamente dirigidos a conseguir un provecho comercial o una compensación monetaria privada. El intercambio de la Obra por otras obras protegidas por derechos de autor, ya sea a través de un sistema para compartir archivos digitales (digital file-sharing) o de cualquier otra manera no será considerado como estar destinado principalmente o dirigido directamente a conseguir un provecho comercial o una compensación monetaria privada, siempre que no se realice un pago mediante una compensación monetaria en relación con el intercambio de obras protegidas por el derecho de autor.

c.	Si usted distribuye, exhibe públicamente, ejecuta públicamente o ejecuta públicamente en forma digital la Obra o cualquier Obra Derivada u Obra Colectiva, Usted debe mantener intacta toda la información de derecho de autor de la Obra y proporcionar, de forma razonable según el medio o manera que Usted esté utilizando: (i) el nombre del Autor Original si está provisto (o seudónimo, si fuere aplicable), y/o (ii) el nombre de la parte o las partes que el Autor Original y/o el Licenciante hubieren designado para la atribución (v.g., un instituto patrocinador, editorial, publicación) en la información de los derechos de autor del Licenciante, términos de servicios o de otras formas razonables; el título de la Obra si está provisto; en la medida de lo razonablemente factible y, si está provisto, el Identificador Uniforme de Recursos (Uniform Resource Identifier) que el Licenciante especifica para ser asociado con la Obra, salvo que tal URI no se refiera a la nota sobre los derechos de autor o a la información sobre el licenciamiento de la Obra; y en el caso de una Obra Derivada, atribuir el crédito identificando el uso de la Obra en la Obra Derivada (v.g., "Traducción Francesa de la Obra del Autor Original," o "Guión Cinematográfico basado en la Obra original del Autor Original"). Tal crédito puede ser implementado de cualquier forma razonable; en el caso, sin embargo, de Obras Derivadas u Obras Colectivas, tal crédito aparecerá, como mínimo, donde aparece el crédito de cualquier otro autor comparable y de una manera, al menos, tan destacada como el crédito de otro autor comparable.

d.	Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:

i.	Regalías por interpretación y ejecución bajo licencias generales. El Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública o la ejecución pública digital de la obra y de recolectar, sea individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, SAYCO), las regalías por la ejecución pública o por la ejecución pública digital de la obra (por ejemplo Webcast) licenciada bajo licencias generales, si la interpretación o ejecución de la obra está primordialmente orientada por o dirigida a la obtención de una ventaja comercial o una compensación monetaria privada.

ii.	Regalías por Fonogramas. El Licenciante se reserva el derecho exclusivo de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, los consagrados por la SAYCO), una agencia de derechos musicales o algún agente designado, las regalías por cualquier fonograma que Usted cree a partir de la obra (“versión cover”) y distribuya, en los términos del régimen de derechos de autor, si la creación o distribución de esa versión cover está primordialmente destinada o dirigida a obtener una ventaja comercial o una compensación monetaria privada.

e.	Gestión de Derechos de Autor sobre Interpretaciones y Ejecuciones Digitales (WebCasting). Para evitar toda confusión, el Licenciante aclara que, cuando la obra sea un fonograma, el Licenciante se reserva el derecho exclusivo de autorizar la ejecución pública digital de la obra (por ejemplo, webcast) y de recolectar, individualmente o a través de una sociedad de gestión colectiva de derechos de autor y derechos conexos (por ejemplo, ACINPRO), las regalías por la ejecución pública digital de la obra (por ejemplo, webcast), sujeta a las disposiciones aplicables del régimen de Derecho de Autor, si esta ejecución pública digital está primordialmente dirigida a obtener una ventaja comercial o una compensación monetaria privada.

5. Representaciones, Garantías y Limitaciones de Responsabilidad.
A MENOS QUE LAS PARTES LO ACORDARAN DE OTRA FORMA POR ESCRITO, EL LICENCIANTE OFRECE LA OBRA (EN EL ESTADO EN EL QUE SE ENCUENTRA) “TAL CUAL”, SIN BRINDAR GARANTÍAS DE CLASE ALGUNA RESPECTO DE LA OBRA, YA SEA EXPRESA, IMPLÍCITA, LEGAL O CUALQUIERA OTRA, INCLUYENDO, SIN LIMITARSE A ELLAS, GARANTÍAS DE TITULARIDAD, COMERCIABILIDAD, ADAPTABILIDAD O ADECUACIÓN A PROPÓSITO DETERMINADO, AUSENCIA DE INFRACCIÓN, DE AUSENCIA DE DEFECTOS LATENTES O DE OTRO TIPO, O LA PRESENCIA O AUSENCIA DE ERRORES, SEAN O NO DESCUBRIBLES (PUEDAN O NO SER ESTOS DESCUBIERTOS). ALGUNAS JURISDICCIONES NO PERMITEN LA EXCLUSIÓN DE GARANTÍAS IMPLÍCITAS, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

6. Limitación de responsabilidad.
A MENOS QUE LO EXIJA EXPRESAMENTE LA LEY APLICABLE, EL LICENCIANTE NO SERÁ RESPONSABLE ANTE USTED POR DAÑO ALGUNO, SEA POR RESPONSABILIDAD EXTRACONTRACTUAL, PRECONTRACTUAL O CONTRACTUAL, OBJETIVA O SUBJETIVA, SE TRATE DE DAÑOS MORALES O PATRIMONIALES, DIRECTOS O INDIRECTOS, PREVISTOS O IMPREVISTOS PRODUCIDOS POR EL USO DE ESTA LICENCIA O DE LA OBRA, AUN CUANDO EL LICENCIANTE HAYA SIDO ADVERTIDO DE LA POSIBILIDAD DE DICHOS DAÑOS. ALGUNAS LEYES NO PERMITEN LA EXCLUSIÓN DE CIERTA RESPONSABILIDAD, EN CUYO CASO ESTA EXCLUSIÓN PUEDE NO APLICARSE A USTED.

7. Término.

a.	Esta Licencia y los derechos otorgados en virtud de ella terminarán automáticamente si Usted infringe alguna condición establecida en ella. Sin embargo, los individuos o entidades que han recibido Obras Derivadas o Colectivas de Usted de conformidad con esta Licencia, no verán terminadas sus licencias, siempre que estos individuos o entidades sigan cumpliendo íntegramente las condiciones de estas licencias. Las Secciones 1, 2, 5, 6, 7, y 8 subsistirán a cualquier terminación de esta Licencia.

b.	Sujeta a las condiciones y términos anteriores, la licencia otorgada aquí es perpetua (durante el período de vigencia de los derechos de autor de la obra). No obstante lo anterior, el Licenciante se reserva el derecho a publicar y/o estrenar la Obra bajo condiciones de licencia diferentes o a dejar de distribuirla en los términos de esta Licencia en cualquier momento; en el entendido, sin embargo, que esa elección no servirá para revocar esta licencia o que deba ser otorgada , bajo los términos de esta licencia), y esta licencia continuará en pleno vigor y efecto a menos que sea terminada como se expresa atrás. La Licencia revocada continuará siendo plenamente vigente y efectiva si no se le da término en las condiciones indicadas anteriormente.

8. Varios.

a.	Cada vez que Usted distribuya o ponga a disposición pública la Obra o una Obra Colectiva, el Licenciante ofrecerá al destinatario una licencia en los mismos términos y condiciones que la licencia otorgada a Usted bajo esta Licencia.

b.	Si alguna disposición de esta Licencia resulta invalidada o no exigible, según la legislación vigente, esto no afectará ni la validez ni la aplicabilidad del resto de condiciones de esta Licencia y, sin acción adicional por parte de los sujetos de este acuerdo, aquélla se entenderá reformada lo mínimo necesario para hacer que dicha disposición sea válida y exigible.

c.	Ningún término o disposición de esta Licencia se estimará renunciada y ninguna violación de ella será consentida a menos que esa renuncia o consentimiento sea otorgado por escrito y firmado por la parte que renuncie o consienta.

d.	Esta Licencia refleja el acuerdo pleno entre las partes respecto a la Obra aquí licenciada. No hay arreglos, acuerdos o declaraciones respecto a la Obra que no estén especificados en este documento. El Licenciante no se verá limitado por ninguna disposición adicional que pueda surgir en alguna comunicación emanada de Usted. Esta Licencia no puede ser modificada sin el consentimiento mutuo por escrito del Licenciante y Usted.
 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