Predictive model of cardiovascular diseases implementing artificial neural networks
Currently, there is a growing need from health entities for the integration of the use of technology. Cardiovascular disease (CEI) identification systems allow a large extent to predict diseases associated with the heart, thus allowing early identification of cardiovascular diseases (CVD) to improve...
- Autores:
-
Henriquez, Carlos
Mardin, Johan
Salcedo, Dixon
PULGAR EMILIANI, MARIA ISABEL
Avendaño Villa, Inirida
Angulo, Luis
Pinedo, Joan
- Tipo de recurso:
- Part of book
- Fecha de publicación:
- 2022
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/10232
- Acceso en línea:
- https://hdl.handle.net/11323/10232
https://repositorio.cuc.edu.co/
- Palabra clave:
- SOM neural networks
GHSOM neural networks
Feature selection
Cardiovascular disease
- Rights
- embargoedAccess
- License
- Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)
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Predictive model of cardiovascular diseases implementing artificial neural networks |
| title |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| spellingShingle |
Predictive model of cardiovascular diseases implementing artificial neural networks SOM neural networks GHSOM neural networks Feature selection Cardiovascular disease |
| title_short |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| title_full |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| title_fullStr |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| title_full_unstemmed |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| title_sort |
Predictive model of cardiovascular diseases implementing artificial neural networks |
| dc.creator.fl_str_mv |
Henriquez, Carlos Mardin, Johan Salcedo, Dixon PULGAR EMILIANI, MARIA ISABEL Avendaño Villa, Inirida Angulo, Luis Pinedo, Joan |
| dc.contributor.author.none.fl_str_mv |
Henriquez, Carlos Mardin, Johan Salcedo, Dixon PULGAR EMILIANI, MARIA ISABEL Avendaño Villa, Inirida Angulo, Luis Pinedo, Joan |
| dc.subject.proposal.eng.fl_str_mv |
SOM neural networks GHSOM neural networks Feature selection Cardiovascular disease |
| topic |
SOM neural networks GHSOM neural networks Feature selection Cardiovascular disease |
| description |
Currently, there is a growing need from health entities for the integration of the use of technology. Cardiovascular disease (CEI) identification systems allow a large extent to predict diseases associated with the heart, thus allowing early identification of cardiovascular diseases (CVD) to improve the quality of life of patients. In this research, a comparative analysis of the results obtained after implementing a series of feature selection techniques (Info. Gain, Gain ratio), and classification techniques based on artificial neural networks (SOM and GHSOM) was carried out, using the same data set “Heart Cleveland Kaggle Disease Data Set” hosted in the Machine Learning UCI repository and under the same test environment. Thus, to establish which of the techniques mentioned achieve a higher percentage of accuracy and precision when identifying patients who suffer from the disease under study. For the performance of the tests, cross-validation was used to select a percentage of the data set to perform them and another for training. Through the implementation of load balancing, normalization, and attribute selection techniques, it was possible to reduce the number of characteristics used in the classification process of the predictive model of cardiovascular diseases, which generated a reduction in computational requirements. Based on the above, 81.45% of successes were obtained with the hybridization of the Gain ratio feature selection technique and the GHSOM training techniques with the use of 7 features. |
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2022 |
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2022-11-16 |
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2023-06-07T14:13:48Z |
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2023-06-07T14:13:48Z 2025-11-16 |
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Henriquez, C. et al. (2022). Predictive Model of Cardiovascular Diseases Implementing Artificial Neural Networks. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_17 |
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https://hdl.handle.net/11323/10232 |
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10.1007/978-3-031-10539-5_17 |
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Henriquez, C. et al. (2022). Predictive Model of Cardiovascular Diseases Implementing Artificial Neural Networks. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_17 978-3-031-10538-8 10.1007/978-3-031-10539-5_17 Corporación Universidad de la Costa REDICUC - Repositorio CUC 978-3-031-10539-5 |
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Computer Information Systems and Industrial Management |
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World Health Organization. https://www.who.int/es/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 15 Jan 2022 Pan American Health Organization. https://www.paho.org/es/temas/hipertension. Accessed 16 Jan 2022 PLOS One Staff. Correction: comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PloS One, 14(10), e0223931 (2019) Mukherjee, T., Robbins, T., Keung, S.N.L.C., Sankar, S., Randeva, H., Arvanitis, T.N.: A systematic review considering risk factors for mortality of patients discharged from hospital with a diagnosis of diabetes. J. Diabetes Complications 34, 107705 (2020) Yao, L., et al.: Enhanced automated diagnosis of coronary artery disease using features extracted from QT interval time series and ST–T waveform. IEEE Access 8, 129510–129524 (2020) Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiological 40(1), 015001 (2019) Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering—a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009). https://doi.org/10.1016/j.infsof.2008.09.009 Manterola, C., Astudillo, P., Arias, E., Claros, N.: Systematic reviews of the literature: what should be known about them. Cir. Esp. 91(3), 149–155 (2013). https://doi.org/10.1016/j.ciresp.2011.07.009 García-Pérez, L., et al.: Systematic review of health-related utilities in Spain: the case of mental health. Gac. Sanit. 28(1), 77–83 (2013). https://doi.org/10.1016/j.gaceta.2013.04.006 Merlano-Porras, C.A., Gorbanev, L.: Health system in Colombia: a systematic review of literature. Rev. Gerencia Políticas Salud 12(24), 74–86 (2013) Sanchez, A., Neira, D., Cabello, J.J.: Frameworks applied in quality management—a systematic review. Rev. Espacios 37(9), 17 (2016) Grams, M.E., et al.: Validation of CKD and related conditions in existing data sets: a systematic review. Amer. J. Kidney Dis. 57(1), 44–54 (2011). https://doi.org/10.1053/j.ajkd.2010.05.013 Crimi, G., et al.: Percutaneous coronary intervention techniques for bifurcation disease: network meta-analysis reveals superiority of double-kissing crush. Can. J. Cardiol. 36(6), 906–914 (2020) Kandasamy, S., Anand, S.S.: Cardiovascular disease among women from vulnerable populations: a review. Can. J. Cardiol. 34(4), 450–457 (2018) Retnakaran, R.: Novel biomarkers for predicting cardiovascular disease in patients with diabetes. Can. J. Cardiol. 34(5), 624–631 (2018) Kramer, A.I., Trinder, M., Brunham, L.R.: Estimating the prevalence of familial hypercholesterolemia in acute coronary syndrome: a systematic review and meta-analysis. Can. J. Cardiol. 35(10), 1322–1331 (2019) Ordovas, K.G., Baldassarre, L.A., Bucciarelli-Ducci, C., et al.: Cardiovascular magnetic resonance in women with cardiovascular disease: position statement from the Society for Cardiovascular Magnetic Resonance (SCMR). J. Cardiovasc Magn. Reson 23, 52 (2021). https://doi.org/10.1186/s12968-021-00746-z Chawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002) Liu, A., Ghosh, J., Martin, C.: Generative oversampling for mining imbalanced datasets. In: de International Conference on Data Mining-DMIN (2007) Sánchez-maroño, V.B.N.: A review of feature selection methods on synthetic data, pp. 483–519 (2013). https://doi.org/10.1007/s10115-012-0487-8 Spolaˆ, N., Monard, M.C.: Label construction for multi-label feature selection (2014). https://doi.org/10.1109/BRACIS.2014.52 Kaur, R., Kumar, G., Kumar, K.: A comparative study of feature selection techniques for intrusion detection. In: de 2nd International Conference on Computing for Sustainable Global Development (2015) Singh, R., Kumar, H., Singla, R.K.: Analysis of Feature Selection Techniques for Network Traffic Dataset. In Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on, (pp. 42–46). IEEE (2013) Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013). https://doi.org/10.1007/s10115-012-0487-8 Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.): Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07353-8 Ibrahim, H.E., Badr, S.M., Shaheen, M.A.: Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems. arXiv preprint arXiv:1210.7650 (2012) Chen, A.M., Lu, H.M., Hecht-Nielsen, R.: Sobre la geometría de las superficies de error de red neuronal de avance. Cálculo neuronal 5(6), 910–927 (1993) Kohonen, T.: Associative memory: a system-theoretical approach, vol. 17. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-96384-1 Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013) Kohonen, T.: Analysis of a simple self-organizing process. Biol. Cybern. 44(2), 135–140 (1982) Dittenbach, M., Merkl, D., Rauber, A.: Organizing and exploring high-dimensional data with the growing hierarchical self-organizing map. In: FSKD, pp. 626–630 (2002) |
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Atribución-NoComercial-CompartirIgual 4.0 Internacional (CC BY-NC-SA 4.0)© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AGhttps://creativecommons.org/licenses/by-nc-sa/4.0/info:eu-repo/semantics/embargoedAccesshttp://purl.org/coar/access_right/c_f1cfHenriquez, CarlosMardin, JohanSalcedo, DixonPULGAR EMILIANI, MARIA ISABELAvendaño Villa, IniridaAngulo, LuisPinedo, Joan2023-06-07T14:13:48Z2025-11-162023-06-07T14:13:48Z2022-11-16Henriquez, C. et al. (2022). Predictive Model of Cardiovascular Diseases Implementing Artificial Neural Networks. In: Saeed, K., Dvorský, J. (eds) Computer Information Systems and Industrial Management. CISIM 2022. Lecture Notes in Computer Science, vol 13293. Springer, Cham. https://doi.org/10.1007/978-3-031-10539-5_17978-3-031-10538-8https://hdl.handle.net/11323/1023210.1007/978-3-031-10539-5_17Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/978-3-031-10539-5Currently, there is a growing need from health entities for the integration of the use of technology. Cardiovascular disease (CEI) identification systems allow a large extent to predict diseases associated with the heart, thus allowing early identification of cardiovascular diseases (CVD) to improve the quality of life of patients. In this research, a comparative analysis of the results obtained after implementing a series of feature selection techniques (Info. Gain, Gain ratio), and classification techniques based on artificial neural networks (SOM and GHSOM) was carried out, using the same data set “Heart Cleveland Kaggle Disease Data Set” hosted in the Machine Learning UCI repository and under the same test environment. Thus, to establish which of the techniques mentioned achieve a higher percentage of accuracy and precision when identifying patients who suffer from the disease under study. For the performance of the tests, cross-validation was used to select a percentage of the data set to perform them and another for training. Through the implementation of load balancing, normalization, and attribute selection techniques, it was possible to reduce the number of characteristics used in the classification process of the predictive model of cardiovascular diseases, which generated a reduction in computational requirements. Based on the above, 81.45% of successes were obtained with the hybridization of the Gain ratio feature selection technique and the GHSOM training techniques with the use of 7 features.1 páginaapplication/pdfengSpringer VerlagGermanyLecture Notes in Computer Science;Computer Information Systems and Industrial ManagementWorld Health Organization. https://www.who.int/es/news-room/fact-sheets/detail/cardiovascular-diseases-(cvds). Accessed 15 Jan 2022Pan American Health Organization. https://www.paho.org/es/temas/hipertension. Accessed 16 Jan 2022PLOS One Staff. Correction: comparison of risk models for mortality and cardiovascular events between machine learning and conventional logistic regression analysis. PloS One, 14(10), e0223931 (2019)Mukherjee, T., Robbins, T., Keung, S.N.L.C., Sankar, S., Randeva, H., Arvanitis, T.N.: A systematic review considering risk factors for mortality of patients discharged from hospital with a diagnosis of diabetes. J. Diabetes Complications 34, 107705 (2020)Yao, L., et al.: Enhanced automated diagnosis of coronary artery disease using features extracted from QT interval time series and ST–T waveform. IEEE Access 8, 129510–129524 (2020)Strodthoff, N., Strodthoff, C.: Detecting and interpreting myocardial infarction using fully convolutional neural networks. Physiological 40(1), 015001 (2019)Kitchenham, B., Brereton, O.P., Budgen, D., Turner, M., Bailey, J., Linkman, S.: Systematic literature reviews in software engineering—a systematic literature review. Inf. Softw. Technol. 51(1), 7–15 (2009). https://doi.org/10.1016/j.infsof.2008.09.009Manterola, C., Astudillo, P., Arias, E., Claros, N.: Systematic reviews of the literature: what should be known about them. Cir. Esp. 91(3), 149–155 (2013). https://doi.org/10.1016/j.ciresp.2011.07.009García-Pérez, L., et al.: Systematic review of health-related utilities in Spain: the case of mental health. Gac. Sanit. 28(1), 77–83 (2013). https://doi.org/10.1016/j.gaceta.2013.04.006Merlano-Porras, C.A., Gorbanev, L.: Health system in Colombia: a systematic review of literature. Rev. Gerencia Políticas Salud 12(24), 74–86 (2013)Sanchez, A., Neira, D., Cabello, J.J.: Frameworks applied in quality management—a systematic review. Rev. Espacios 37(9), 17 (2016)Grams, M.E., et al.: Validation of CKD and related conditions in existing data sets: a systematic review. Amer. J. Kidney Dis. 57(1), 44–54 (2011). https://doi.org/10.1053/j.ajkd.2010.05.013Crimi, G., et al.: Percutaneous coronary intervention techniques for bifurcation disease: network meta-analysis reveals superiority of double-kissing crush. Can. J. Cardiol. 36(6), 906–914 (2020)Kandasamy, S., Anand, S.S.: Cardiovascular disease among women from vulnerable populations: a review. Can. J. Cardiol. 34(4), 450–457 (2018)Retnakaran, R.: Novel biomarkers for predicting cardiovascular disease in patients with diabetes. Can. J. Cardiol. 34(5), 624–631 (2018)Kramer, A.I., Trinder, M., Brunham, L.R.: Estimating the prevalence of familial hypercholesterolemia in acute coronary syndrome: a systematic review and meta-analysis. Can. J. Cardiol. 35(10), 1322–1331 (2019)Ordovas, K.G., Baldassarre, L.A., Bucciarelli-Ducci, C., et al.: Cardiovascular magnetic resonance in women with cardiovascular disease: position statement from the Society for Cardiovascular Magnetic Resonance (SCMR). J. Cardiovasc Magn. Reson 23, 52 (2021). https://doi.org/10.1186/s12968-021-00746-zChawla, N.V., Bowyer, K.W., Hall, L.O., Kegelmeyer, W.P.: SMOTE: synthetic minority over-sampling technique. J. Artif. Intell. Res. 16, 321–357 (2002)Liu, A., Ghosh, J., Martin, C.: Generative oversampling for mining imbalanced datasets. In: de International Conference on Data Mining-DMIN (2007)Sánchez-maroño, V.B.N.: A review of feature selection methods on synthetic data, pp. 483–519 (2013). https://doi.org/10.1007/s10115-012-0487-8Spolaˆ, N., Monard, M.C.: Label construction for multi-label feature selection (2014). https://doi.org/10.1109/BRACIS.2014.52Kaur, R., Kumar, G., Kumar, K.: A comparative study of feature selection techniques for intrusion detection. In: de 2nd International Conference on Computing for Sustainable Global Development (2015)Singh, R., Kumar, H., Singla, R.K.: Analysis of Feature Selection Techniques for Network Traffic Dataset. In Machine Intelligence and Research Advancement (ICMIRA), 2013 International Conference on, (pp. 42–46). IEEE (2013)Bolón-Canedo, V., Sánchez-Maroño, N., Alonso-Betanzos, A.: A review of feature selection methods on synthetic data. Knowl. Inf. Syst. 34(3), 483–519 (2013). https://doi.org/10.1007/s10115-012-0487-8Kumar Kundu, M., Mohapatra, D.P., Konar, A., Chakraborty, A. (eds.): Advanced Computing, Networking and Informatics- Volume 1. SIST, vol. 27. Springer, Cham (2014). https://doi.org/10.1007/978-3-319-07353-8Ibrahim, H.E., Badr, S.M., Shaheen, M.A.: Adaptive layered approach using machine learning techniques with gain ratio for intrusion detection systems. arXiv preprint arXiv:1210.7650 (2012)Chen, A.M., Lu, H.M., Hecht-Nielsen, R.: Sobre la geometría de las superficies de error de red neuronal de avance. Cálculo neuronal 5(6), 910–927 (1993)Kohonen, T.: Associative memory: a system-theoretical approach, vol. 17. Springer, Heidelberg (2012). https://doi.org/10.1007/978-3-642-96384-1Kohonen, T.: Essentials of the self-organizing map. Neural Netw. 37, 52–65 (2013)Kohonen, T.: Analysis of a simple self-organizing process. Biol. Cybern. 44(2), 135–140 (1982)Dittenbach, M., Merkl, D., Rauber, A.: Organizing and exploring high-dimensional data with the growing hierarchical self-organizing map. 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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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