Time series forecasting for tuberculosis incidence employing neural network models
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...
| Main Authors: | , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-07-01
|
| Series: | Heliyon |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2405844022011859 |
| _version_ | 1811222114122858496 |
|---|---|
| author | Alvaro David Orjuela-Cañón Andres Leonardo Jutinico Mario Enrique Duarte González Carlos Enrique Awad García Erika Vergara María Angélica Palencia |
| author_facet | Alvaro David Orjuela-Cañón Andres Leonardo Jutinico Mario Enrique Duarte González Carlos Enrique Awad García Erika Vergara María Angélica Palencia |
| author_sort | Alvaro David Orjuela-Cañón |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-04-12T08:11:10Z |
| format | Article |
| id | doaj.art-62c785b157694c37b13f9f3bd80d6ac7 |
| institution | Directory Open Access Journal |
| issn | 2405-8440 |
| language | English |
| last_indexed | 2024-04-12T08:11:10Z |
| publishDate | 2022-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Heliyon |
| spelling | doaj.art-62c785b157694c37b13f9f3bd80d6ac72022-12-22T03:40:57ZengElsevierHeliyon2405-84402022-07-0187e09897Time series forecasting for tuberculosis incidence employing neural network modelsAlvaro David Orjuela-Cañón0Andres Leonardo Jutinico1Mario Enrique Duarte González2Carlos Enrique Awad García3Erika Vergara4María Angélica Palencia5School of Medicine and Health Sciences, Universidad del Rosario, Bogotá, D.C., Colombia; Corresponding author.Mechanical, Electronics and Biomedical Engineering Faculty, Universidad Antonio Nariño, Bogotá, D.C., ColombiaMechanical, Electronics and Biomedical Engineering Faculty, Universidad Antonio Nariño, Bogotá, D.C., ColombiaSubred Integrada de Servicios de Salud Centro Oriente, Bogotá, D.C., ColombiaMechanical, Electronics and Biomedical Engineering Faculty, Universidad Antonio Nariño, Bogotá, D.C., ColombiaSubred Integrada de Servicios de Salud Centro Oriente, Bogotá, D.C., ColombiaEvery 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.http://www.sciencedirect.com/science/article/pii/S2405844022011859TuberculosisTime seriesForecastingNeural networksMachine learning |
| spellingShingle | Alvaro David Orjuela-Cañón Andres Leonardo Jutinico Mario Enrique Duarte González Carlos Enrique Awad García Erika Vergara María Angélica Palencia Time series forecasting for tuberculosis incidence employing neural network models Heliyon Tuberculosis Time series Forecasting Neural networks Machine learning |
| title | Time series forecasting for tuberculosis incidence employing neural network models |
| title_full | Time series forecasting for tuberculosis incidence employing neural network models |
| title_fullStr | Time series forecasting for tuberculosis incidence employing neural network models |
| title_full_unstemmed | Time series forecasting for tuberculosis incidence employing neural network models |
| title_short | Time series forecasting for tuberculosis incidence employing neural network models |
| title_sort | time series forecasting for tuberculosis incidence employing neural network models |
| topic | Tuberculosis Time series Forecasting Neural networks Machine learning |
| url | http://www.sciencedirect.com/science/article/pii/S2405844022011859 |
| work_keys_str_mv | AT alvarodavidorjuelacanon timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels AT andresleonardojutinico timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels AT marioenriqueduartegonzalez timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels AT carlosenriqueawadgarcia timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels AT erikavergara timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels AT mariaangelicapalencia timeseriesforecastingfortuberculosisincidenceemployingneuralnetworkmodels |