Prediction of mortality due to Covid 19 in Peru using artificial neural networks

• Autores: Mayta Avalos , Cesar, Valdivia Mamani , Jesús Cristian, Rosales Castilla, Fernando, Gines Colana , Milca
• Formato: artículo
• Fecha de Publicación: 2021
• Institución: Universidad La Salle
• Repositorio: Revistas - Universidad La Salle
• Lenguaje: español
• OAI Identifier: oai:ojs.revistas.ulasalle.edu.pe:article/43
• Enlace del recurso: https://revistas.ulasalle.edu.pe/innosoft/article/view/43; https://doi.org/10.48168/innosoft.s6.a43; https://purl.org/42411/s6/a43; https://n2t.net/ark:/42411/s6/a43
• Nivel de acceso: acceso abierto
• Materia: Artificial Intelligence; Time Series; COVID 19; Prediction; Forecast; Inteligencia Artificial; Series Temporales; Predicción; Pronóstico

With the development of the pandemic in Peru, the number of deaths has been increasing and unfortunately the appropriate measures have not been taken, this because we do not have a tool that allows us to know the number of possible deaths in a given time. The objective of this article is to propose a tool capable of predicting the number of deaths from COVID-19 as a function of time. The methodology used was artificial neural networks using time series with information obtained from the Ministry of Health of the Peruvian state through its open data portal. The results achieved had a mean square error of 0.0037 and a loss of 0.0480. The results obtained throughout the article confirm the validity of this tool and its effectiveness in predicting the number of deaths from COVID 19.