Bibliographic citations
Molina, J., Castillo, V. (2024). Modelo de análisis predictivo para mejorar el control de trastornos alimenticios en adolescentes de Lima Metropolitana basado en Machine Learning [Tesis, Universidad Peruana de Ciencias Aplicadas (UPC)]. http://hdl.handle.net/10757/682134
Molina, J., Castillo, V. Modelo de análisis predictivo para mejorar el control de trastornos alimenticios en adolescentes de Lima Metropolitana basado en Machine Learning [Tesis]. PE: Universidad Peruana de Ciencias Aplicadas (UPC); 2024. http://hdl.handle.net/10757/682134
@misc{renati/1299647,
title = "Modelo de análisis predictivo para mejorar el control de trastornos alimenticios en adolescentes de Lima Metropolitana basado en Machine Learning",
author = "Castillo Revelo, Valeria Angelica",
publisher = "Universidad Peruana de Ciencias Aplicadas (UPC)",
year = "2024"
}
This paper presents a predictive analysis model of eating disorders in adolescents in Metropolitan Lima, based on Machine Learning. It addresses the problem by identifying patterns and trends during data collection and analysis, using algorithms and learning techniques to improve predictive accuracy. Project objectives include problem analysis, model design, validation through expert testing, and the creation of a continuity plan, covering preventive measures, information security, contingency plans and team responsibilities. Project management focuses on defining inclusions and exclusions to establish a clear scope, as well as creating plans that address quality, costs, requirements, communications, resources, risks and scope. The importance of identifying resources and requirements for the development and implementation of the model, with deadlines to ensure that objectives are achieved, is stressed. The model not only seeks to prevent eating disorders to improve the quality of life of adolescents, but is also intended as a valuable resource and support for medical centers specializing in psychology.
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