Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques
Overweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classifica...
- Autores:
-
Calderón Díaz, Mailyn
Serey Castillo, Leonardo J.
Vallejos Cuevas, Esperanza A.
Espinoza, Alexis
Salas, Rodrigo
Macías Jiménez, Mayra A.
- 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:11323/13825
- Acceso en línea:
- https://hdl.handle.net/11323/13825
https://repositorio.cuc.edu.co/
- Palabra clave:
- Machine learning
Biochemical profiles
Lipid profiles
Normal-weight
Overweight
Obesity
Classification
- Rights
- openAccess
- License
- Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)
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Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| title |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| spellingShingle |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques Machine learning Biochemical profiles Lipid profiles Normal-weight Overweight Obesity Classification |
| title_short |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| title_full |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| title_fullStr |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| title_full_unstemmed |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| title_sort |
Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniques |
| dc.creator.fl_str_mv |
Calderón Díaz, Mailyn Serey Castillo, Leonardo J. Vallejos Cuevas, Esperanza A. Espinoza, Alexis Salas, Rodrigo Macías Jiménez, Mayra A. |
| dc.contributor.author.none.fl_str_mv |
Calderón Díaz, Mailyn Serey Castillo, Leonardo J. Vallejos Cuevas, Esperanza A. Espinoza, Alexis Salas, Rodrigo Macías Jiménez, Mayra A. |
| dc.subject.proposal.eng.fl_str_mv |
Machine learning Biochemical profiles Lipid profiles Normal-weight Overweight Obesity Classification |
| topic |
Machine learning Biochemical profiles Lipid profiles Normal-weight Overweight Obesity Classification |
| description |
Overweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions. |
| publishDate |
2023 |
| dc.date.issued.none.fl_str_mv |
2023-03-17 |
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2024-11-25T18:01:39Z |
| dc.date.available.none.fl_str_mv |
2024-11-25T18:01:39Z |
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Artículo de revista |
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Mailyn Calderón-Díaz, Leonardo J. Serey-Castillo, Esperanza A. Vallejos-Cuevas, Alexis Espinoza, Rodrigo Salas, Mayra A. Macías-Jiménez, Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques., Procedia Computer Science, Volume 220, 2023, Pages 978-983, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.135. |
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https://hdl.handle.net/11323/13825 |
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10.1016/j.procs.2023.03.135 |
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1877-0509 |
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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 |
Mailyn Calderón-Díaz, Leonardo J. Serey-Castillo, Esperanza A. Vallejos-Cuevas, Alexis Espinoza, Rodrigo Salas, Mayra A. Macías-Jiménez, Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques., Procedia Computer Science, Volume 220, 2023, Pages 978-983, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.135. 10.1016/j.procs.2023.03.135 1877-0509 Corporación Universidad de la Costa REDICUC - Repositorio CUC |
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https://hdl.handle.net/11323/13825 https://repositorio.cuc.edu.co/ |
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eng |
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eng |
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Procedia computer science |
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WHO. (2021). Obesity and overweight. WHO. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweight Fan, H., & Zhang, X. (2022). "Influence of parental weight change on the incidence of overweight and obesity in offspring". BMC Pediatrics, 22(1). https://doi.org/10.1186/s12887-022-03399-8 Nelson, T. D., Haugen, K. A., Resetar Volz, J. L., Zhe, E. J., Axelrod, M. I., Spear Filigno, S., Stevens, A. L., & Lundahl, A. (2015). "Overweight and obesity among youth entering residential care: Prevalence and correlates". Residential Treatment for Children and Youth, 32(2), 99– 112. https://doi.org/10.1080/0886571X.2015.1043786 Lafontaine, T. (2008). "Physical Activity: The Epidemic of Obesity and Overweight Among Youth: Trends, Consequences, and Interventions". American Journal of Lifestyle Medicine, 2(1), 30–36. https://doi.org/10.1177/1559827607309688 Skogen, I. B., & Høydal, K. L. (2021). "Adolescents who are overweight or obese-the relevance of a social network to engaging in physical activity: a qualitative study". BMC Public Health, 21(701). https://doi.org/10.1186/s12889-021-10727-7 Banna, J. (2019). "Obesity Prevention in Children in Latin America Through Interventions Using Technology". American Journal of Lifestyle Medicine, 13(2), 138–141. https://doi.org/10.1177/1559827618823320 Cuevas, A., Alvarez, V., & Olivos, C. (2009). "The emerging obesity problem in Latin America". Expert Review of Cardiovascular Therapy, 7(3), 281–288. https://doi.org/10.1586/14779072.7.3.281 Corvalán, C., Garmendia, M. L., Jones-Smith, J., Lutter, C. K., Miranda, J. J., Pedraza, L. S., Popkin, B. M., Ramirez-Zea, M., Salvo, D., & Stein, A. D. (2017). "Nutrition status of children in Latin America". Obesity Reviews, 18, 7–18. https://doi.org/10.1111/OBR.12571 OECD. (2021). Overweight or obese population (indicator). https://www.oecd-ilibrary.org/social-issues-migration-health/overweight-or-obesepopulation/indicator/english_86583552-en Vio, F., & Kain, J. (2019). "Descripción de la progresión de la obesidad y enfermedades relacionadas en Chile". Revista Médica de Chile, 147(9), 1114–1121. http://dx.doi.org/10.4067/s0034-98872019000901114 Alghamdi, A. S., Yahya, M. A., Alshammari, G. M., & Osman, M. A. (2017). "Prevalence of overweight and obesity among police officers in Riyadh City and risk factors for cardiovascular disease". Lipids in Health and Disease, 16(79). https://doi.org/10.1186/s12944-017-0467-9 Yin, R., Wang, X., Li, K., Yu, K., & Yang, L. (2021). "Lipidomic profiling reveals distinct differences in plasma lipid composition in overweight or obese adolescent students". BMC Endocrine Disorders, 21(201). https://doi.org/10.1186/s12902-021-00859-7 Pengpid, S., & Peltzer, K. (2015). "Overweight and obesity and associated factors among school-aged adolescents in six pacific island countries in Oceania". International Journal of Environmental Research and Public Health, 12(11), 14505–14518. https://doi.org/10.3390/ijerph121114505 Dunstan, J., Aguirre, M., Bastías, M., Nau, C., Glass, T. A., & Tobar, F. (2020). "Predicting nationwide obesity from food sales using machine learning". Health Informatics Journal, 26(1), 652–663. https://doi.org/10.1177/1460458219845959 Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). "Involvement of Machine Learning Tools in Healthcare Decision Making". Journal of Healthcare Engineering, 2021, 1–20. https://doi.org/10.1155/2021/6679512 Fernández-Juan, A., Ramírez-Gil, C., & van der Werf, L. (2016). "La valoración antropométrica en el contexto de la escuela como medida para detectar y prevenir efectos a largo plazo de la obesidad y del sobrepeso en niños en edad escolar". Revista Colombiana de Cardiologia, 23(5), 435–442. https://doi.org/10.1016/j.rccar.2016.06.007 Babajide, O., Tawfik, H., Palczewska, A., Gorbenko, A., Astrup, A., Martínez, A., Oppert, J. M., & Sørensen, T. I. A. (2020). "A machine learning approach to short-term body weight prediction in a dietary intervention program". Computational Science – ICCS 2020, 12140 LNCS, 441–455. https://doi.org/10.1007/978-3-030-50423-6_33 Du, Y., Rafferty, A. R., McAuliffe, F. M., Wei, L., & Mooney, C. (2022). "An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus". Scientific Reports, 12(1). https://doi.org/10.1038/s41598-022-05112-2 Mahadevaswamy, U. B., Keerthana, R., Pooja, B. B., Sangatya, V., & Supritha, S. (2022). "A Hybrid Model approach for Heart Disease Prediction". 2022 IEEE 2nd Mysore Sub Section International Conference, 1–6. https://doi.org/10.1109/MysuruCon55714.2022.9972516 Omkar, M., & Nimala, K. (2022). "Machine Learning based Diabetes Prediction using with AWS cloud". 2022 International Conference on Innovative Computing, Intelligent Communication and Smart Electrical Systems (ICSES), 1–7. https://doi.org/10.1109/icses55317.2022.9914160 Chen, T., & Guestrin, C. (2016). "XGBoost: A scalable tree boosting system". KDD ’16: Proceedings of the ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 13-17-August-2016, 785–794. https://doi.org/10.1145/2939672.2939785 Chen, T., & Guestrin, C. (2016). "XGBoost: A Scalable Tree Boosting System". Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785–794. https://doi.org/10.1145/2939672.2939785 Likitha, B., Nakka, J., Verma, J., & Naik, N. S. (2021). "Prediction of Breast Cancer Analysis Using Machine Learning Algorithms and XGBoost Technique". In K. R. Venugopal, P. D. Shenoy, R. Buyya, L. M. Patnaik, & S. S. Iyengar (Eds.), Data Science and Computational Intelligence (pp. 298–313). Springer International Publishing. Huo, L., Tan, Y., Wang, S., Geng, C., Li, Y., Ma, X., Wang, B., He, Y., Yao, C., & Ouyang, T. (2021). "Machine learning models to improve the differentiation between benign and malignant breast lesions on ultrasound: A multicenter external validation study". Cancer Management and Research, 13, 3367–3379. https://doi.org/10.2147/CMAR.S297794. Chilyabanyama, O. N., Chilengi, R., Simuyandi, M., Chisenga, C. C., Chirwa, M., Hamusonde, K., Saroj, R. K., Iqbal, N. T., Ngaruye, I., & Bosomprah, S. (2022). "Performance of Machine Learning Classifiers in Classifying Stunting among Under-Five Children in Zambia". Children, 9(7). https://doi.org/10.3390/children9071082 Santisteban Quiroz, J. P. (2022). "Estimation of obesity levels based on dietary habits and condition physical using computational intelligence". Informatics in Medicine Unlocked, 29. https://doi.org/10.1016/j.imu.2022.100901 Xia, Y., Liu, C., Li, Y. Y., & Liu, N. (2017). "A boosted decision tree approach using Bayesian hyper-parameter optimization for credit scoring". Expert Systems with Applications, 78, 225–241. https://doi.org/10.1016/j.eswa.2017.02.017 Mozaffarian, D., Benjamin, E. J., Go, A. S., Arnett, D. K., Blaha, M. J., Cushman, M., Das, S. R., Ferranti, S. de, Després, J. P., Fullerton, H. J., Howard, V. J., Huffman, M. D., Isasi, C. R., Jiménez, M. C., Judd, S. E., Kissela, B. M., Lichtman, J. H., Lisabeth, L. D., Liu, S., … Turner, M. B. (2016). "Heart Disease and Stroke Statistics—2016 Update". Circulation, 133(4), e38–e48. https://doi.org/10.1161/CIR.0000000000000350 Hwang, S., Kim, H.-E., M.D., J. J., & Kim, H.-J. (2016). "A novel approach for tuberculosis screening based on deep convolutional neural networks". Proc. SPIE 9785, Medical Imaging 2016: Computer-Aided Diagnosis, 9785, 750–757. https://doi.org/10.1117/12.2216198 Hjorth, M. F., Zohar, Y., Hill, J. O., & Astrup, A. (2018). "Personalized Dietary Management of Overweight and Obesity Based on Measures of Insulin and Glucose". Annual Review of Nutrition. https://doi.org/10.1146/annurev-nutr-082117 Chatterjee, A., Gerdes, M. W., & Martinez, S. G. (2020). "Identification of risk factors associated with obesity and overweight—a machine learning overview". Sensors (Switzerland), 20(9). https://doi.org/10.3390/s20092734 Kwon, Y. J., Lee, H. S., & Lee, J. W. (2018). "Direct bilirubin is associated with low-density lipoprotein subfractions and particle size in overweight and centrally obese women". Nutrition, Metabolism and Cardiovascular Diseases, 28(10), 1021–1028. https://doi.org/10.1016/j.numecd.2018.05.013 Nascimento, H., Alves, A. I., Coimbra, S., Catarino, C., Gomes, D., Bronze-Da-Rocha, E., Costa, E., Rocha-Pereira, P., Aires, L., Mota, J., Ferreira Mansilha, H., Rêgo, C., Santos-Silva, A., & Belo, L. (2015). "Bilirubin is independently associated with oxidized LDL levels in young obese patients". Diabetology and Metabolic Syndrome, 7(1), 1–5. https://doi.org/10.1186/1758-5996-7-4/TABLES/2 |
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Atribución-NoComercial-SinDerivadas 4.0 Internacional (CC BY-NC-ND 4.0)© 2023 The Authors.https://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Calderón Díaz, MailynSerey Castillo, Leonardo J.Vallejos Cuevas, Esperanza A.Espinoza, AlexisSalas, RodrigoMacías Jiménez, Mayra A.2024-11-25T18:01:39Z2024-11-25T18:01:39Z2023-03-17Mailyn Calderón-Díaz, Leonardo J. Serey-Castillo, Esperanza A. Vallejos-Cuevas, Alexis Espinoza, Rodrigo Salas, Mayra A. Macías-Jiménez, Detection of variables for the diagnosis of overweight and obesity in young Chileans using machine learning techniques., Procedia Computer Science, Volume 220, 2023, Pages 978-983, ISSN 1877-0509, https://doi.org/10.1016/j.procs.2023.03.135.https://hdl.handle.net/11323/1382510.1016/j.procs.2023.03.1351877-0509Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Overweight and obesity are considered epidemic problems. The number of factors involved in developing extra body fat makes harder the detection of this problem. Therefore, among the several variables and their levels presented in overweight and obese people, there is a need to improve the classification of people with these conditions. To this aim, in this paper, we conducted a variable analysis from biochemical and lipid profiles in young Chileans with normal weight, overweight, and obesity using machine learning techniques. XGBoost library was selected as the classifier. 21 variables (13 from biochemical and 8 from lipid profiles) were chosen as features. 100 iterations were conducted, and an 80% cross-validation was obtained. The variables with greater relevance in the classification task were total cholesterol, glycemia, LDH enzyme, bilirubin, and VLDL cholesterol. All of these, except bilirubin, are consistent with previous research in which these features have been used to assess risk factors for developing overweight or obesity. Then, further research must include a deep study regarding bilirubin's influence over these conditions.6 páginasapplication/pdfengElsevier B.V.Netherlandshttps://www.sciencedirect.com/science/article/pii/S1877050923006701?via%3Dihub#keys0001Detection of variables for the diagnosis of overweight and obesity in young chileans using machine learning techniquesArtículo 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_970fb48d4fbd8a85Procedia computer scienceWHO. (2021). Obesity and overweight. WHO. https://www.who.int/news-room/fact-sheets/detail/obesity-and-overweightFan, H., & Zhang, X. (2022). "Influence of parental weight change on the incidence of overweight and obesity in offspring". BMC Pediatrics, 22(1). https://doi.org/10.1186/s12887-022-03399-8Nelson, T. D., Haugen, K. A., Resetar Volz, J. L., Zhe, E. J., Axelrod, M. I., Spear Filigno, S., Stevens, A. L., & Lundahl, A. (2015). "Overweight and obesity among youth entering residential care: Prevalence and correlates". Residential Treatment for Children and Youth, 32(2), 99– 112. https://doi.org/10.1080/0886571X.2015.1043786Lafontaine, T. (2008). "Physical Activity: The Epidemic of Obesity and Overweight Among Youth: Trends, Consequences, and Interventions". American Journal of Lifestyle Medicine, 2(1), 30–36. https://doi.org/10.1177/1559827607309688Skogen, I. B., & Høydal, K. L. (2021). "Adolescents who are overweight or obese-the relevance of a social network to engaging in physical activity: a qualitative study". BMC Public Health, 21(701). https://doi.org/10.1186/s12889-021-10727-7Banna, J. (2019). "Obesity Prevention in Children in Latin America Through Interventions Using Technology". American Journal of Lifestyle Medicine, 13(2), 138–141. https://doi.org/10.1177/1559827618823320Cuevas, A., Alvarez, V., & Olivos, C. (2009). "The emerging obesity problem in Latin America". Expert Review of Cardiovascular Therapy, 7(3), 281–288. https://doi.org/10.1586/14779072.7.3.281Corvalán, C., Garmendia, M. L., Jones-Smith, J., Lutter, C. K., Miranda, J. J., Pedraza, L. S., Popkin, B. M., Ramirez-Zea, M., Salvo, D., & Stein, A. D. (2017). "Nutrition status of children in Latin America". Obesity Reviews, 18, 7–18. https://doi.org/10.1111/OBR.12571OECD. (2021). Overweight or obese population (indicator). https://www.oecd-ilibrary.org/social-issues-migration-health/overweight-or-obesepopulation/indicator/english_86583552-enVio, F., & Kain, J. (2019). "Descripción de la progresión de la obesidad y enfermedades relacionadas en Chile". Revista Médica de Chile, 147(9), 1114–1121. http://dx.doi.org/10.4067/s0034-98872019000901114Alghamdi, A. S., Yahya, M. A., Alshammari, G. M., & Osman, M. A. (2017). "Prevalence of overweight and obesity among police officers in Riyadh City and risk factors for cardiovascular disease". Lipids in Health and Disease, 16(79). https://doi.org/10.1186/s12944-017-0467-9Yin, R., Wang, X., Li, K., Yu, K., & Yang, L. (2021). "Lipidomic profiling reveals distinct differences in plasma lipid composition in overweight or obese adolescent students". BMC Endocrine Disorders, 21(201). https://doi.org/10.1186/s12902-021-00859-7Pengpid, S., & Peltzer, K. (2015). "Overweight and obesity and associated factors among school-aged adolescents in six pacific island countries in Oceania". International Journal of Environmental Research and Public Health, 12(11), 14505–14518. https://doi.org/10.3390/ijerph121114505Dunstan, J., Aguirre, M., Bastías, M., Nau, C., Glass, T. A., & Tobar, F. (2020). "Predicting nationwide obesity from food sales using machine learning". Health Informatics Journal, 26(1), 652–663. https://doi.org/10.1177/1460458219845959Jayatilake, S. M. D. A. C., & Ganegoda, G. U. (2021). "Involvement of Machine Learning Tools in Healthcare Decision Making". Journal of Healthcare Engineering, 2021, 1–20. https://doi.org/10.1155/2021/6679512Fernández-Juan, A., Ramírez-Gil, C., & van der Werf, L. (2016). "La valoración antropométrica en el contexto de la escuela como medida para detectar y prevenir efectos a largo plazo de la obesidad y del sobrepeso en niños en edad escolar". Revista Colombiana de Cardiologia, 23(5), 435–442. https://doi.org/10.1016/j.rccar.2016.06.007Babajide, O., Tawfik, H., Palczewska, A., Gorbenko, A., Astrup, A., Martínez, A., Oppert, J. M., & Sørensen, T. I. A. (2020). "A machine learning approach to short-term body weight prediction in a dietary intervention program". Computational Science – ICCS 2020, 12140 LNCS, 441–455. https://doi.org/10.1007/978-3-030-50423-6_33Du, Y., Rafferty, A. R., McAuliffe, F. M., Wei, L., & Mooney, C. (2022). "An explainable machine learning-based clinical decision support system for prediction of gestational diabetes mellitus". 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ara ejercer estos derechos sobre la Obra tal y como se indica a continuación:</p>
    <ol type="a">
      <li>Reproducir la Obra, incorporar la Obra en una o más Obras Colectivas, y reproducir la Obra incorporada en las Obras Colectivas.</li>
      <li>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.</li>
      <li>Distribuir copias de las Obras Derivadas que se generen, exhibirlas públicamente, ejecutarlas públicamente y/o ponerlas a disposición pública.</li>
    </ol>
    <p>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).</p>
  </li>
  <br/>
  <li>
    Restricciones.
    <p>La licencia otorgada en la anterior Sección 3 está expresamente sujeta y limitada por las siguientes restricciones:</p>
    <ol type="a">
      <li>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).</li>
      <li>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.</li>
      <li>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.</li>
      <li>
        Para evitar toda confusión, el Licenciante aclara que, cuando la obra es una composición musical:
        <ol type="i">
          <li>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.</li>
          <li>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.</li>
        </ol>
      </li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
  <li>
    Representaciones, Garantías y Limitaciones de Responsabilidad.
    <p>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.</p>
  </li>
  <br/>
  <li>
    Limitación de responsabilidad.
    <p>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.</p>
  </li>
  <br/>
  <li>
    Término.
    <ol type="a">
      <li>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.</li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
  <li>
    Varios.
    <ol type="a">
      <li>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.</li>
      <li>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.</li>
      <li>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.</li>
      <li>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.</li>
    </ol>
  </li>
  <br/>
</ol>
 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