Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data

Authors

DOI:

https://doi.org/10.33448/rsd-v10i4.13879

Keywords:

Machine learning; Neural network; Chagas disease.

Abstract

Objective: evaluate the potential use of machine learning and the automatic selection of attributes in discrimination of individuals with and without Chagas disease based on clinical and sociodemographic data. Method: After the evaluation of many learning algorithms, they have been chosen and the comparison between neural network Multilayer Perceptron (MLP) and the Linear Regression (LR) was done, seeking which one presents the best performance for prediction of the Chagas disease diagnosis, being used the criteria of sensitivity, specificity, accuracy and area under the ROC curve (AUC). Generated models were also compared, using the methods of automatic selection of attributes: Forward Selection, Backward Elimination and genetic algorithm. Results: The best results were achieved using the genetic algorithm and the MLP presented accuracy of 95.95%, 78.30% sensitivity, and specificity of 75.00% and AUC of 0.861. Conclusion: It was proved to be a very interesting performance, given the nature of the data used for sorting and use in public health, glimpsing its relevance in the medical field, enabling an approximation of prevalence that justifies the actions of active search of individuals Chagas disease patients for treatment and prevention.

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Published

06/04/2021

How to Cite

TELES, W. de S. .; MACHADO, A. P. .; CANTOS JÚNIOR, P. C. C. .; MELO, C. M. de .; SILVA, M. H. S. .; SILVA, R. N. da .; JERALDO, V. de L. S. . Machine learning and automatic selection of attributes for the identification of Chagas disease from clinical and sociodemographic data. Research, Society and Development, [S. l.], v. 10, n. 4, p. e19310413879, 2021. DOI: 10.33448/rsd-v10i4.13879. Disponível em: https://rsdjournal.org/index.php/rsd/article/view/13879. Acesso em: 14 apr. 2021.

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Section

Health Sciences