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Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014

1
Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, México
2
Unidad Académica de Odontología, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, México
3
CONACYT—Universidad Autónoma de Zacatecas—Jardín Juarez 147, Centro, Zacatecas 98000, Zac, Mexico
*
Author to whom correspondence should be addressed.
These authors contributed equally to this work.
Bioengineering 2018, 5(2), 47; https://doi.org/10.3390/bioengineering5020047
Received: 31 May 2018 / Revised: 7 June 2018 / Accepted: 15 June 2018 / Published: 18 June 2018
Oral health represents an essential component in the quality of life of people, being a determinant factor in general health since it may affect the risk of suffering other conditions, such as chronic diseases. Oral diseases have become one of the main public health problems, where dental caries is the condition that most affects oral health worldwide, occurring in about 90% of the global population. This condition has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused depending on the consumption of certain nutritional elements interacting simultaneously with different factors, such as socioeconomic factors. Based on this problem, an analysis of a set of 189 dietary and demographic determinants is performed in this work, in order to find the relationship between these factors and the oral situation of a set of subjects. The oral situation refers to the presence and absence/restorations of caries. The methodology is performed constructing a dense artificial neural network (ANN), as a computer-aided diagnosis tool, looking for a generalized model that allows for classifying subjects. As validation, the classification model was evaluated through a statistical analysis based on a cross validation, calculating the accuracy, loss function, receiving operating characteristic (ROC) curve and area under the curve (AUC) parameters. The results obtained were statistically significant, obtaining an accuracy ≃ 0.69 and AUC values of 0.69 and 0.75. Based on these results, it is possible to conclude that the classification model developed through the deep ANN is able to classify subjects with absence of caries from subjects with presence or restorations with high accuracy, according to their demographic and dietary factors. View Full-Text
Keywords: NHANES; oral health; dental caries; classification multivariate models; computer-aided diagnosis; artificial neural networks; deep learning; statistical analysis NHANES; oral health; dental caries; classification multivariate models; computer-aided diagnosis; artificial neural networks; deep learning; statistical analysis
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MDPI and ACS Style

Zanella-Calzada, L.A.; Galván-Tejada, C.E.; Chávez-Lamas, N.M.; Rivas-Gutierrez, J.; Magallanes-Quintanar, R.; Celaya-Padilla, J.M.; Galván-Tejada, J.I.; Gamboa-Rosales, H. Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014. Bioengineering 2018, 5, 47. https://doi.org/10.3390/bioengineering5020047

AMA Style

Zanella-Calzada LA, Galván-Tejada CE, Chávez-Lamas NM, Rivas-Gutierrez J, Magallanes-Quintanar R, Celaya-Padilla JM, Galván-Tejada JI, Gamboa-Rosales H. Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014. Bioengineering. 2018; 5(2):47. https://doi.org/10.3390/bioengineering5020047

Chicago/Turabian Style

Zanella-Calzada, Laura A.; Galván-Tejada, Carlos E.; Chávez-Lamas, Nubia M.; Rivas-Gutierrez, Jesús; Magallanes-Quintanar, Rafael; Celaya-Padilla, Jose M.; Galván-Tejada, Jorge I.; Gamboa-Rosales, Hamurabi. 2018. "Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014" Bioengineering 5, no. 2: 47. https://doi.org/10.3390/bioengineering5020047

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