Галерея 3369143

Галерея 3369143

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Predictive Model Based on Machine Learning for the Detection of Physically Mistreated Women in the Peruvian Scope
Published: 21 January 2020 Publication History
ICAAI '19: Proceedings of the 3rd International Conference on Advances in Artificial Intelligence
In-Cooperation Northumbria University: University of Northumbria at Newcastle
Qualifiers research-article Research Refereed limited
R. Collins, Violence. Princeton: Princeton University Press, 2008. DOI: http://dx.doi.org/10.1515/9781400831753 Google Scholar Cross Ref World Health Organization, "Global status report on violence prevention 2014," 2014. Google Scholar ENDES - Instituto Nacional de Estadística e Informática, "Perú: Encuesta Demográfica y de Salud Familiar 2015," Encuesta Demográfica y de Salud Familiar, 2015. [Online]. Available: http://iinei.inei.gob.pe/microdatos/Consulta_por_Encuesta.asp. [Accessed: 26-Jul-2019]. Google Scholar Instituto Nacional de Estadística e Informática, "Peru: Línea de base de los principales indicadores disponibles de los objetivos de desarrollo sostenible (ODS) 2016," Lima, 2017. Google Scholar C. E. Murray, A. M. Pow, A. Chow, H. Nemati, and J. White, "Domestic Violence Service Providers' Needs and Perceptions of Technology: A Qualitative Study," J. Technol. Hum. Serv., vol. 33, no. 2, pp. 133--155, 2015. DOI: https://doi.org/10.1080/15228835.2014.1000558 Google Scholar Cross Ref Organización de las Naciones Unidas, "Informe Anual 2017-2018 de ONU Mujeres," 2018. Google Scholar L. L. HEISE, "Violence Against Women: an integrated, ecological framework," Violence Against Women, vol. 4, no. 3, pp. 262--290, Jun. 1998. DOI: https://doi.org/10.1177/1077801298004003002 Google Scholar Cross Ref K. Starmer, "Domestic violence: The facts, the issues, the future," Int. Rev. Law, Comput. Technol., vol. 25, no. 1--2, pp. 9--15, 2011. DOI: https://doi.org/10.1080/13600869.2011.594643 Google Scholar Digital Library [S. W. Mihalic and D. Elliot, "A social learning theory model of marital violence," J. Fam. Violence, vol. 12, no. 1, pp. 21--47, 1997. DOI: https://doi.org/10.1023/A:1021941816102 Google Scholar Cross Ref M. A. Straus, R. J. Gelles, and S. K. Stienmetz, Behind Closed Doors. New York: Routledge, 2017. DOI: https://doi.org/10.4324/9781351298681 Google Scholar Cross Ref A. C. McClellan and M. R. Killeen, "Attachment Theory and Violence Toward Women by Male Intimate Partners," J. Nurs. Scholarsh., vol. 32, no. 4, pp. 353--360, Dec. 2000. DOI: 10.1111/j.1547-5069.2000.00353.x Google Scholar Cross Ref Ministerio de Salud, Guías Nacionales de Atención Integral de la Salud Sexual y Reproductiva. Lima: Gráfica Ñañez S.A., 2004. Google Scholar S. Marsland, Machine Learning, 2nd ed. Taylor & Francis Gorup, 2015. Google Scholar G. Rebala, A. Ravi, and S. Churiwala, "An introduction to machine learning methods," in Machine Learning Methods for Ecological Applications, S. Nature, Ed. Boston, MA: Springer US, 2019, pp. 1--35. DOI: https://doi.org/10.1007/978-1-4615-5289-5_1 Google Scholar P. Geurts, D. Ernst, and L. Wehenkel, "Extremely randomized trees," Mach. Learn., vol. 63, no. 1, pp. 3--42, Apr. 2006. Google Scholar Digital Library P. Geurts and G. Louppe, "Learning to rank with extremely randomized trees," JMLR Work. Conf. Proc., vol. 14, pp. 49--61, 2011. Google Scholar Instituto Nacional de Estadística e Informática, "Consultas por encuestas del Instituto Nacional de Estadística e Informática," 2018. [Online]. Available: http://iinei.inei.gob.pe/microdatos/index.htm. Google Scholar
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Violence against women in recent years in Peru showed alarming results, where 65.0 % of them were victims of some type of violence at some time in their lives. The study consisted of the elaboration of a predictive model that allows the recognition of the future physically mistreated woman. The data used was from a public survey of national scope. The methodology specified the data obtained; three supervised learning models Random Forest Classifier, Decision Tree Classifier, and Extra Trees Classifier were developed with the intention of buying the results and selecting that of the best performance. Regarding the results, Random Forest Classifier was the best model to obtain a precision of 51.0 % and a recall of 40.0 %, above those obtained by the other algorithms used. Subsequently, the best model passed to a calibration process where the average score was 0.7182, the precision was 0.76, and the recall was 0.31; concluding that the model classifies 76.0% of cases as physically mistreated women of which 31.0 % are effectively mistreated.
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Галерея 3369143

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