Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria

Austeclino Magalhães Barros; Angelo Amâncio Duarte; Manoel Barral Netto; Bruno Bezerril Andrade

doi:10.1109/HEALTH.2010.5556584

Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria

Austeclino Magalhães Barros
, Angelo Amâncio Duarte
, Manoel Barral Netto
, Bruno Bezerril Andrade

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

In the preset study, Artificial Neural Network (ANN) and Bayesian Network (BN) techniques are evaluated as supporting tools for the diagnosis of asymptomatic malaria infection. These techniques are compared with two classical laboratorial tests for diagnosis of malaria: the light microscopy and the Nested peR. To do this, the tests were run in a group of 380 individuals from the Brazilian Amazon. The results indicate that both innovative techniques are able to identify asymptomatically infected individuals with better accuracy than the microscopy test and are potentially useful for helping the diagnosis of asymptomatic malaria.

Original language	English (US)
Title of host publication	12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010
Publisher	IEEE Computer Society
Pages	106-111
Number of pages	6
ISBN (Print)	9781424463749
DOIs	https://doi.org/10.1109/HEALTH.2010.5556584
State	Published - 2010
Externally published	Yes

Publication series

Name	12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010

Keywords

Asymptomatic malaria diagnosis
Bayesian Network
Component:Artificial Neural Network

ASJC Scopus subject areas

Computer Networks and Communications
Computer Science Applications
Health Informatics
Health Information Management

Access to Document

10.1109/HEALTH.2010.5556584

Cite this

Barros, A. M., Duarte, A. A., Netto, M. B., & Andrade, B. B. (2010). Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria. In 12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010 (pp. 106-111). Article 5556584 (12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010). IEEE Computer Society. https://doi.org/10.1109/HEALTH.2010.5556584

Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria. / Barros, Austeclino Magalhães; Duarte, Angelo Amâncio; Netto, Manoel Barral et al.
12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010. IEEE Computer Society, 2010. p. 106-111 5556584 (12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Barros, AM, Duarte, AA, Netto, MB & Andrade, BB 2010, Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria. in 12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010., 5556584, 12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010, IEEE Computer Society, pp. 106-111. https://doi.org/10.1109/HEALTH.2010.5556584

Barros AM, Duarte AA, Netto MB, Andrade BB. Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria. In 12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010. IEEE Computer Society. 2010. p. 106-111. 5556584. (12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010). doi: 10.1109/HEALTH.2010.5556584

Barros, Austeclino Magalhães ; Duarte, Angelo Amâncio ; Netto, Manoel Barral et al. / Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria. 12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010. IEEE Computer Society, 2010. pp. 106-111 (12th IEEE International Conference on e-Health Networking, Application and Services, Healthcom 2010).

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abstract = "In the preset study, Artificial Neural Network (ANN) and Bayesian Network (BN) techniques are evaluated as supporting tools for the diagnosis of asymptomatic malaria infection. These techniques are compared with two classical laboratorial tests for diagnosis of malaria: the light microscopy and the Nested peR. To do this, the tests were run in a group of 380 individuals from the Brazilian Amazon. The results indicate that both innovative techniques are able to identify asymptomatically infected individuals with better accuracy than the microscopy test and are potentially useful for helping the diagnosis of asymptomatic malaria.",

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AB - In the preset study, Artificial Neural Network (ANN) and Bayesian Network (BN) techniques are evaluated as supporting tools for the diagnosis of asymptomatic malaria infection. These techniques are compared with two classical laboratorial tests for diagnosis of malaria: the light microscopy and the Nested peR. To do this, the tests were run in a group of 380 individuals from the Brazilian Amazon. The results indicate that both innovative techniques are able to identify asymptomatically infected individuals with better accuracy than the microscopy test and are potentially useful for helping the diagnosis of asymptomatic malaria.

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Artificial neural networks and Bayesian networks as supportting tools for diagnosis of asymptomatic malaria

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Keywords

ASJC Scopus subject areas

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