TY - JOUR
T1 - Una revisión sistemática de Modelos de clasificación de dengue utilizando machine learning
AU - Maquen-Niño, Gisella Luisa Elena
AU - Bravo, Jessie
AU - Alarcón, Roger
AU - Adrianzén-Olano, Ivan
AU - Vega-Huerta, Hugo
N1 - Publisher Copyright:
© 2023, Associacao Iberica de Sistemas e Tecnologias de Informacao. All rights reserved.
PY - 2023
Y1 - 2023
N2 - Dengue is an arboviral disease that annually reports a large number of infected on the north coast and the Peruvian jungle. According to statistics, it is increasing yearly. This article aims to develop a systematic review of the scientific literature on the study variables and the machine learning methods currently used for detecting dengue infection. The methodology used was PRISMA, initially mapping the literature of 274 scientific articles, leaving 33 articles selected for the systematic review. The results obtained are that the most used machine learning algorithms are neural networks (NN) and support vector machine (SVM). Likewise, it has been found that scientists tend to carry out research with climatic or demographic variables to obtain better results. It is concluded that the machine learning methods that have been used the most are neural networks of different types: convolutional, recurrent, deep, and multilayer, and for the prediction of dengue outbreaks the time series methods with LSTM and ARIMA were the predominant ones, it was also established that the trend is towards the inclusion of climatic and demographic variables in the prediction models.
AB - Dengue is an arboviral disease that annually reports a large number of infected on the north coast and the Peruvian jungle. According to statistics, it is increasing yearly. This article aims to develop a systematic review of the scientific literature on the study variables and the machine learning methods currently used for detecting dengue infection. The methodology used was PRISMA, initially mapping the literature of 274 scientific articles, leaving 33 articles selected for the systematic review. The results obtained are that the most used machine learning algorithms are neural networks (NN) and support vector machine (SVM). Likewise, it has been found that scientists tend to carry out research with climatic or demographic variables to obtain better results. It is concluded that the machine learning methods that have been used the most are neural networks of different types: convolutional, recurrent, deep, and multilayer, and for the prediction of dengue outbreaks the time series methods with LSTM and ARIMA were the predominant ones, it was also established that the trend is towards the inclusion of climatic and demographic variables in the prediction models.
KW - Dengue
KW - artificial neural networks
KW - classification algorithms
KW - classification methods
KW - detection
KW - machine learning
KW - random forest
KW - support vector machine
UR - http://www.scopus.com/inward/record.url?scp=85174169325&partnerID=8YFLogxK
U2 - 10.17013/risti.50.5-27
DO - 10.17013/risti.50.5-27
M3 - Artículo
AN - SCOPUS:85174169325
SN - 1646-9895
VL - 2023
SP - 5
EP - 27
JO - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
JF - RISTI - Revista Iberica de Sistemas e Tecnologias de Informacao
IS - 50
ER -