Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014
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...
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| Format: | Article |
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MDPI AG
2018-06-01
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| Series: | Bioengineering |
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| Online Access: | http://www.mdpi.com/2306-5354/5/2/47 |
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| author | Laura A. Zanella-Calzada Carlos E. Galván-Tejada Nubia M. Chávez-Lamas Jesús Rivas-Gutierrez Rafael Magallanes-Quintanar Jose M. Celaya-Padilla Jorge I. Galván-Tejada Hamurabi Gamboa-Rosales |
| author_facet | Laura A. Zanella-Calzada Carlos E. Galván-Tejada Nubia M. Chávez-Lamas Jesús Rivas-Gutierrez Rafael Magallanes-Quintanar Jose M. Celaya-Padilla Jorge I. Galván-Tejada Hamurabi Gamboa-Rosales |
| author_sort | Laura A. Zanella-Calzada |
| collection | DOAJ |
| description | 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. |
| first_indexed | 2024-03-12T07:57:11Z |
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| institution | Directory Open Access Journal |
| issn | 2306-5354 |
| language | English |
| last_indexed | 2024-03-12T07:57:11Z |
| publishDate | 2018-06-01 |
| publisher | MDPI AG |
| record_format | Article |
| series | Bioengineering |
| spelling | doaj.art-c2d8f018db4c45bd9a960582b4ff7ec92023-09-02T20:11:34ZengMDPI AGBioengineering2306-53542018-06-01524710.3390/bioengineering5020047bioengineering5020047Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014Laura A. Zanella-Calzada0Carlos E. Galván-Tejada1Nubia M. Chávez-Lamas2Jesús Rivas-Gutierrez3Rafael Magallanes-Quintanar4Jose M. Celaya-Padilla5Jorge I. Galván-Tejada6Hamurabi Gamboa-Rosales7Unidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoUnidad Académica de Odontología, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoUnidad Académica de Odontología, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoCONACYT—Universidad Autónoma de Zacatecas—Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MexicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoUnidad Académica de Ingeniería Eléctrica, Universidad Autónoma de Zacatecas, Jardín Juarez 147, Centro, Zacatecas 98000, Zac, MéxicoOral 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.http://www.mdpi.com/2306-5354/5/2/47NHANESoral healthdental cariesclassification multivariate modelscomputer-aided diagnosisartificial neural networksdeep learningstatistical analysis |
| spellingShingle | Laura A. Zanella-Calzada Carlos E. Galván-Tejada Nubia M. Chávez-Lamas Jesús Rivas-Gutierrez Rafael Magallanes-Quintanar Jose M. Celaya-Padilla Jorge I. Galván-Tejada Hamurabi Gamboa-Rosales Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 Bioengineering NHANES oral health dental caries classification multivariate models computer-aided diagnosis artificial neural networks deep learning statistical analysis |
| title | Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 |
| title_full | Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 |
| title_fullStr | Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 |
| title_full_unstemmed | Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 |
| title_short | Deep Artificial Neural Networks for the Diagnostic of Caries Using Socioeconomic and Nutritional Features as Determinants: Data from NHANES 2013–2014 |
| title_sort | deep artificial neural networks for the diagnostic of caries using socioeconomic and nutritional features as determinants data from nhanes 2013 2014 |
| topic | NHANES oral health dental caries classification multivariate models computer-aided diagnosis artificial neural networks deep learning statistical analysis |
| url | http://www.mdpi.com/2306-5354/5/2/47 |
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