Prediction of psychosocial risks in teachers using data mining
Integrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are not yet explored and that contribute to strategic decision-making processes, suc...
- Autores:
-
Viloria, Amelec
Rodríguez López, Jorge
Orellano Llinás, Nataly
Vargas Mercado, Carlos
León Coronado, Luz Estela
Negrete Sepulveda, Ana María
Pineda, Omar
- Tipo de recurso:
- Article of journal
- Fecha de publicación:
- 2020
- Institución:
- Corporación Universidad de la Costa
- Repositorio:
- REDICUC - Repositorio CUC
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.cuc.edu.co:11323/7783
- Acceso en línea:
- https://hdl.handle.net/11323/7783
https://doi.org/10.1007/978-981-15-3125-5_50
https://repositorio.cuc.edu.co/
- Palabra clave:
- Support vector machine
Naïve bayes
Genetic algorithms
- Rights
- openAccess
- License
- Attribution-NonCommercial-NoDerivatives 4.0 International
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Prediction of psychosocial risks in teachers using data mining |
| title |
Prediction of psychosocial risks in teachers using data mining |
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Prediction of psychosocial risks in teachers using data mining Support vector machine Naïve bayes Genetic algorithms |
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Prediction of psychosocial risks in teachers using data mining |
| title_full |
Prediction of psychosocial risks in teachers using data mining |
| title_fullStr |
Prediction of psychosocial risks in teachers using data mining |
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Prediction of psychosocial risks in teachers using data mining |
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Prediction of psychosocial risks in teachers using data mining |
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Viloria, Amelec Rodríguez López, Jorge Orellano Llinás, Nataly Vargas Mercado, Carlos León Coronado, Luz Estela Negrete Sepulveda, Ana María Pineda, Omar |
| dc.contributor.author.spa.fl_str_mv |
Viloria, Amelec Rodríguez López, Jorge Orellano Llinás, Nataly Vargas Mercado, Carlos León Coronado, Luz Estela Negrete Sepulveda, Ana María Pineda, Omar |
| dc.subject.spa.fl_str_mv |
Support vector machine Naïve bayes Genetic algorithms |
| topic |
Support vector machine Naïve bayes Genetic algorithms |
| description |
Integrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are not yet explored and that contribute to strategic decision-making processes, such as machine learning techniques or machine learning. In occupational safety and health management systems, it is important to carry out the proper follow-ups and process controls in any type of industry and organization whose relationship is direct. This paper presents the application of three methods related to data mining: Support Vector Machine algorithms, Naïve Bayes, and Genetic Algorithms to identify the degree of psychosocial risk in university teachers of the Mumbai University in India. The use of SVM easily recognizes physiological variables and the best prediction performance was achieved with 96.34% accuracy efficiency. |
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2020 |
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2021-01-28T12:57:12Z |
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2021-01-28T12:57:12Z |
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eng |
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1. Viloria A, Bucci N, Luna M (2017) Historical development of psychosocial risk assessment models. J Eng Appl Sci 12(11):2915–2919. ISSN: 1816-949X. Medwell Journals. 2. Viloria A, Bucci N, Luna M (2017) Comparative analysis between psychosocial risk assessment models. J Eng Appl Sci 12(11):2901–2903. ISSN: 1816-949X. Medwell Journals 3. Instituto Sindical de Trabajo, Ambiente y Salud (ISTAS). 4. Organización Mundial de la Salud (2007) Commission on social determinants of health. A conceptual framework for action on the social determinants of health. (Discussion paper. Geneve: Retrieved from 5. Moncada S, Llorens C, Navarro A, Kristensen T (2005) Versión en lengua castellana del cuestionario psicosocial de Copenhague (COPSOQ). La Societat Catalana de Seguretat i Medicina del Treball. España 6. Serra J (2011) Pautas para la intervención Psicosocial en las organizaciones. Taller para gestionar el estrés y otros riesgos psicosociales. Reto laboral del siglo XXI. 7. Viloria A, Bucci N, Luna M (2017) Comparative analysis between psychosocial risk assessment models. J Eng Appl Sci 12(11):2901–2903. ISSN: 1816-949X. Medwell Journals 8. Caamaño AJ, Echeverría MM, Retamal VO, Navarro CT, Espinosa FT (2015) Modelo predictivo de fuga de clientes utilizando minería de datos para una empresa de telecomunicaciones en chile. Universidad Ciencia y Tecnología, 18(72) 9. Mark Hall y otros 5 autores (2009) The WEKA data mining software: an update; SIGKDD explorations 11(1) 10. Bucci N, Luna M (2012) Contrastación entre los Modelos de Estudio del Estrés como Soporte para la Evaluación de los Riesgos Psicosociales en el Trabajo. Revista Digital de Investigación y Postgrado de la Universidad Nacional Experimental Politécnica “Antonio José de Sucre”, Vicerrectorado Barquisimeto. Venezuela 2(1):21–38 abril 2012. ISSN: 2244-7393 11. Agarwal B, Mittal N (2014) Text classification using machine learning methods—a survey. In: Proceedings of the second international conference on soft computing for problem solving (SocProS 2012), Springer, New Delhi, 28–30 Dec 2012, pp 701–709 12. Larrañaga P, Inza I, Moujahid A (2016) Tema 6. Clasificadores Bayesianos. Departamento de Ciencias de la Computación e Inteligencia Artificial. En línea: http://www.sc.ehu.es/ccwbayes/docencia/mmcc/docs/t6bayesianos.pdf Acceso: 9 de enero de 2016, Universidad del País Vasco-Euskal Herriko Unibertsitatea, Españ 13. Quinlan JR (1993) C4.5: programs for machine learning. Elsevier, Burlington 14. García DA (2007) Algoritmo de discretización de series de tiempo basado en entropía y su aplicación en datos colposcopicos. Tesis de Maestría en Inteligencia Artificial. Universidad Veracruzana, México 15. Corso CL (2009) Alternativa de herramienta libre para la implementación de aprendizaje automático. En línea: http://www.investigacion.frc.utn.edu.ar/labsis/Publicaciones/congresos_labsis/cynthia/Alternativa_de_herramienta_para_Mineria_Datos_CNEISI_2009.pdf. Acceso: 10 de agosto de 2015, Argentina 16. Anon D (2016) Búsqueda exhaustiva. En línea: http://dis.um.es/~domingo/apuntes/AlgBio/1213/exhaustiva.pdf. Acceso: 2 de agosto 2015, Universidad de Murcia, España 17. Hepner GF (1990) Artificial neural network classification using a minimal training set. Comparison to conventional supervised classification. Photogramm Eng Remote Sens 56(4):469–473 18. Bucci N, Luna M, Viloria A, García JH, Parody A, Varela N, López LAB (2018) Factor analysis of the psychosocial risk assessment instrument. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. Springer, Berlin |
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Viloria, AmelecRodríguez López, JorgeOrellano Llinás, NatalyVargas Mercado, CarlosLeón Coronado, Luz EstelaNegrete Sepulveda, Ana MaríaPineda, Omar2021-01-28T12:57:12Z2021-01-28T12:57:12Z2020https://hdl.handle.net/11323/7783https://doi.org/10.1007/978-981-15-3125-5_50Corporación Universidad de la CostaREDICUC - Repositorio CUChttps://repositorio.cuc.edu.co/Integrated management systems aim to improve these everyday situations that are inherent to work and cause for concern. In search for continuous improvement, it is necessary to innovate with techniques in areas that are not yet explored and that contribute to strategic decision-making processes, such as machine learning techniques or machine learning. In occupational safety and health management systems, it is important to carry out the proper follow-ups and process controls in any type of industry and organization whose relationship is direct. This paper presents the application of three methods related to data mining: Support Vector Machine algorithms, Naïve Bayes, and Genetic Algorithms to identify the degree of psychosocial risk in university teachers of the Mumbai University in India. The use of SVM easily recognizes physiological variables and the best prediction performance was achieved with 96.34% accuracy efficiency.Viloria, AmelecRodríguez López, JorgeOrellano Llinás, NatalyVargas Mercado, Carlos-will be generated-orcid-0000-0002-5436-0568-600León Coronado, Luz EstelaNegrete Sepulveda, Ana MaríaPineda, Omar-will be generated-orcid-0000-0002-8239-3906-600application/pdfengCorporación Universidad de la CostaAttribution-NonCommercial-NoDerivatives 4.0 Internationalhttp://creativecommons.org/licenses/by-nc-nd/4.0/info:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Lecture Notes in Electrical Engineeringhttps://link.springer.com/chapter/10.1007/978-981-15-3125-5_50Support vector machineNaïve bayesGenetic algorithmsPrediction of psychosocial risks in teachers using data miningArtículo de revistahttp://purl.org/coar/resource_type/c_6501http://purl.org/coar/resource_type/c_2df8fbb1Textinfo:eu-repo/semantics/articlehttp://purl.org/redcol/resource_type/ARTinfo:eu-repo/semantics/acceptedVersion1. Viloria A, Bucci N, Luna M (2017) Historical development of psychosocial risk assessment models. J Eng Appl Sci 12(11):2915–2919. ISSN: 1816-949X. Medwell Journals.2. Viloria A, Bucci N, Luna M (2017) Comparative analysis between psychosocial risk assessment models. J Eng Appl Sci 12(11):2901–2903. ISSN: 1816-949X. Medwell Journals3. Instituto Sindical de Trabajo, Ambiente y Salud (ISTAS).4. Organización Mundial de la Salud (2007) Commission on social determinants of health. A conceptual framework for action on the social determinants of health. (Discussion paper. Geneve: Retrieved from5. Moncada S, Llorens C, Navarro A, Kristensen T (2005) Versión en lengua castellana del cuestionario psicosocial de Copenhague (COPSOQ). La Societat Catalana de Seguretat i Medicina del Treball. España6. Serra J (2011) Pautas para la intervención Psicosocial en las organizaciones. Taller para gestionar el estrés y otros riesgos psicosociales. Reto laboral del siglo XXI.7. Viloria A, Bucci N, Luna M (2017) Comparative analysis between psychosocial risk assessment models. J Eng Appl Sci 12(11):2901–2903. ISSN: 1816-949X. Medwell Journals8. Caamaño AJ, Echeverría MM, Retamal VO, Navarro CT, Espinosa FT (2015) Modelo predictivo de fuga de clientes utilizando minería de datos para una empresa de telecomunicaciones en chile. Universidad Ciencia y Tecnología, 18(72)9. Mark Hall y otros 5 autores (2009) The WEKA data mining software: an update; SIGKDD explorations 11(1)10. Bucci N, Luna M (2012) Contrastación entre los Modelos de Estudio del Estrés como Soporte para la Evaluación de los Riesgos Psicosociales en el Trabajo. Revista Digital de Investigación y Postgrado de la Universidad Nacional Experimental Politécnica “Antonio José de Sucre”, Vicerrectorado Barquisimeto. Venezuela 2(1):21–38 abril 2012. ISSN: 2244-739311. Agarwal B, Mittal N (2014) Text classification using machine learning methods—a survey. In: Proceedings of the second international conference on soft computing for problem solving (SocProS 2012), Springer, New Delhi, 28–30 Dec 2012, pp 701–70912. Larrañaga P, Inza I, Moujahid A (2016) Tema 6. Clasificadores Bayesianos. Departamento de Ciencias de la Computación e Inteligencia Artificial. En línea: http://www.sc.ehu.es/ccwbayes/docencia/mmcc/docs/t6bayesianos.pdf Acceso: 9 de enero de 2016, Universidad del País Vasco-Euskal Herriko Unibertsitatea, Españ13. Quinlan JR (1993) C4.5: programs for machine learning. Elsevier, Burlington14. García DA (2007) Algoritmo de discretización de series de tiempo basado en entropía y su aplicación en datos colposcopicos. Tesis de Maestría en Inteligencia Artificial. Universidad Veracruzana, México15. Corso CL (2009) Alternativa de herramienta libre para la implementación de aprendizaje automático. En línea: http://www.investigacion.frc.utn.edu.ar/labsis/Publicaciones/congresos_labsis/cynthia/Alternativa_de_herramienta_para_Mineria_Datos_CNEISI_2009.pdf. Acceso: 10 de agosto de 2015, Argentina16. Anon D (2016) Búsqueda exhaustiva. En línea: http://dis.um.es/~domingo/apuntes/AlgBio/1213/exhaustiva.pdf. Acceso: 2 de agosto 2015, Universidad de Murcia, España17. Hepner GF (1990) Artificial neural network classification using a minimal training set. Comparison to conventional supervised classification. Photogramm Eng Remote Sens 56(4):469–47318. Bucci N, Luna M, Viloria A, García JH, Parody A, Varela N, López LAB (2018) Factor analysis of the psychosocial risk assessment instrument. In: Tan Y, Shi Y, Tang Q (eds) Data mining and big data. DMBD 2018. Lecture notes in computer science, vol 10943. 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