Multivariate features selection from demographic and dietary descriptors as caries risk determinants in oral health diagnosis: Data from NHANES 2013–2014

Zanella-Calzada, Laura A.; Galván-Tejada, Carlos E.; Chávez-Lamas, Nubia M.; Galván-Tejada, Jorge I.; Celaya-Padilla, José M.

doi:10.1109/conielecomp.2018.8327202

Cited by 3 publications

(2 citation statements)

References 16 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In this work [ 21 ] three different models obtained through a fast backward selection (FBS) of features are presented, each model corresponding to a different age group, then a posterior evaluation using a net reclassification improvement (NRI) technique is performed, besides the AUC parameter, obtaining a maximum true positive-true negative rate of 0.787. On the other hand, this work [ 22 ] presents a multivariate model obtained through a FBS method based on the p -value, in order to classify between three different classes, ”caries”, ”restorations” and ”control”; this model was evaluated using a statistical analysis, obtaining a maximum AUC value of 0.664. Finally, this work [ 23 ] presents a univariate analysis using a linear regression approach in order to classify between subjects with the presence of caries and with absence; then, from the most significant univariate models, a multivariate model contained by three features was developed, which obtained an AUC value of 0.572.…”

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

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.

show abstract

Section: Methodsmentioning

confidence: 99%

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

et al. 2018

View full text Add to dashboard Cite

show abstract

“…Caries is defined as a preventable disease of chronic multifactorial origin that affects the hard tissues of the tooth, with a prevalence of 94% of individuals worldwide [27]. People with diabetes have an increased risk of gum inflammation (periodontitis) or gingival hyperplasia if their blood glucose is not properly managed.…”

Section: Introductionmentioning

confidence: 99%

A Preliminary Approach for Finding Linked Determinants between Diabetes and Caries using Genetic Algorithms

Zanella-Calzada¹,

Galván-Tejada²,

Chávez-Lamas³

et al. 2019

RCS

Self Cite

View full text Add to dashboard Cite

Two of the public health conditions that continue having an increase in their incidence and prevalence worldwide are dental caries and diabetes mellitus. Dental caries is a multifactorial disease that affects the hard tissues of the tooth, being the most widespread disease in man; while diabetes mellitus is a multifactorial chronic disease that involves a group of disorders that alter the production and use of insulin, being of the main causes of disease in high, medium and low income countries. In this work is proposed the use of genetic algorithms in order to select the most significant features from a database of demographic data obtained from the NHANES 2013-2014, that best describe the difference between cases (diabetes and caries) and controls (healthy), according to their health status. After the feature selection, it is performed a validation stage, evaluating the multivariate model contained by these features. Within the results achieved shows a fitness > 0.980 and an AUC = 0.975, validating that the demographic features selected in this paper present a highly significant capacity to classify subjects that present caries and diabetes simultaneously from healthy subjects.

show abstract

Evaluation of Linked Determinants Between Diabetes and Caries

et al. 2019

View full text Add to dashboard Cite

Multivariate features selection from demographic and dietary descriptors as caries risk determinants in oral health diagnosis: Data from NHANES 2013–2014

Cited by 3 publications

References 16 publications

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

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

A Preliminary Approach for Finding Linked Determinants between Diabetes and Caries using Genetic Algorithms

Evaluation of Linked Determinants Between Diabetes and Caries

Contact Info

Product

Resources

About