期刊
HELIYON
卷 8, 期 7, 页码 -出版社
CELL PRESS
DOI: 10.1016/j.heliyon.2022.e09897
关键词
Tuberculosis; Time series; Forecasting; Neural networks; Machine learning
资金
- Ministerio de Ciencia, Tecnologia e Innovacion -Miniciencias, in Colombia [123380762899]
This study used artificial neural networks to predict the development trend of tuberculosis and found that traditional models performed better, which can help health authorities propose more effective control strategies.
Every effort aimed at stopping the expansion of Tuberculosis is important to national programs' struggle to combat this disease. Different computational tools have been proposed in order to design new strategies that allow managing potential patients and thus providing the correct treatment. In this work, artificial neural networks were used for time series forecasting, which were trained with information on reported cases obtained from the national vigilance institution in Colombia. Three neural models were proposed in order to determine the best one according to their forecasting performance. The first approach employed a nonlinear autoregressive model, the second proposal used a recurrent neural network, and the third proposal was based on radial basis functions. The results are presented in terms of the mean average percentage error, which indicates that the models based on traditional methods show better performance compared to connectionist ones. These models contribute to obtaining dynamic information about incidence, thus providing extra-help for health authorities to propose more strategies to control the disease's spread.
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