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This work proposes the analysis of a set of features related to breastfeeding and dental caries. The data was taken from NHANES 2013–2014, for the development of the model, the random forest algorithm is implemented, was evaluated through a statistical analysis based on the out-of-bag error (OOB), the receiver operating characteristic (ROC) curve and the area under the curve (AUC). Results; AUC = 0.669 and OOB = 36% were observed, which shows that the multivariate model developed is statistically significant and achieves an adequate generalization for the classification of the subjects. Conclusions; A model based on information related to breastfeeding is able to classify subjects with presence of dental caries in a generalized way, obtaining preliminary results for the development of a support tool for the diagnosis of this condition based on a new approach.

Dental caries and diabetes mellitus, represent two chronic diseases of public health that present statistics of high prevalence worldwide. One of the main problems that affect these conditions is demography, since the least developed countries are those that have less access to public health services and are limited by the economic costs that represent the treatments. In this work is presented the univariate analysis, of a set of demographic determinants obtained from the National Health and Nutrition Examination Surveys (NHANES), in order to identify those that present the most significant information related to estimation of these diseases. According to the results, the age of the subjects is the main determinant describing the simultaneous presence of these diseases, obtaining an area under the curve (AUC) value \(\ge \) 0.846, concluding that age can be used as tool for their preventive diagnosis.

Depression is a mental disorder which typically includes recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, and in severe cases fatigue, causing inability to perform daily activities, leading to a progressive loss of quality of life. Monitoring depression (unipolar and bipolar patients) stats relays on traditional method reports from patients; however, bias is commonly present, given the patients’ interpretation of the experiences. Nevertheless, to overcome this problem, Ecological Momentary Assessment (EMA) reports have been proposed and widely used. These reports includes data of the behaviour, feelings, and other type of activities recorded almost in real time using different types of portable devices, which nowadays include smartphones and other wearables such as smartwatches. In this study is proposed a methodology to detect depressive patients with the motion data generated by patient activity, recorded with a smartband, obtained from the “Depresjon” database. Using this signal as information source, a feature extraction approach of statistical features, in time and spectral evolution of the signal, is done. Subsequently, a clever feature selection with a genetic algorithm approach is done to reduce the amount of information required to give a fast noninvasive diagnostic. Results show that the feature extraction approach can achieve a value of 0.734 of area under the curve (AUC), and after applying feature selection approach, a model comprised by two features from the motion signal can achieve a 0.647 AUC. These results allow us to conclude that using the activity signal from a smartband, it is possible to distinguish between depressive states, providing a preliminary and automated tool to specialists for the diagnosis of depression almost in real time.

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.

One of the principal conditions that affects oral health worldwide is dental caries, occurring in about 90% of the global population. This pathology has been considered a challenge because of its high prevalence, besides being a chronic but preventable disease which can be caused by a series of different demographic, dietary" among others. Based on this problem, in this research a demographic and dietary features analysis is performed for the classification of subjects according to their oral health status based on caries, according to the age group where the population belongs, using as feature selector a technique based on fast backward selection (FBS) approach for the development of three predictive models, one for each age range (group 1: 10⁻19; group 2: 20⁻59; group 3: 60 or more years old). As validation, a net reclassification improvement (NRI), AUC, ROC, and OR values are used to evaluate their classification accuracy. We analyzed 189 demographic and dietary features from National Health and Nutrition Examination Survey (NHANES) 2013⁻2014. Each model obtained statistically significant results for most features and narrow OR confidence intervals. Age group 2 obtained a mean NRI = -0.080 and AUC = 0.933; age group 3 obtained a mean NRI = -0.024 and AUC = 0.787; and age group 4 obtained a mean NRI = -0.129 and AUC = 0.735. Based on these results, it is concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status in patients based on their likelihood of developing caries, and the age group could imply different risk factors for subjects.

The main pathology that make up the epidemiological profile of oral health status is dental caries. This pathology is considered a challenge because its high prevalence, besides being a chronic but preventable disease, which can be caused by a series of different demographic, dietary and laboratory data, among others. Therefore, in this research two multivariate models were proposed for the classification between control patients (‘0’), presence of caries (‘1’) and presence of restorations (‘2’). Analyzing 189 demographic and dietary features from NHANES 2013–2014, by FBS method based on the P-value they were reduced to 24 features, which were separated in two sets according to their demographic or dietary information. Both datasets were subjected to a statistical analysis based on linear regression, obtaining their residuals, AUC, ROC and OR values, in order to validate their classification accuracy. For each model were obtained significant statistical results and none of them presented problems related to outliers, according to their residuals. Both models shown P-values > 0.05 for most features and small OR confidence intervals. Finally, demographic data model obtained an AUC = 0.664 and dietary data model obtained an AUC = 0.633, averaging the AUC values for each outcome, proving to be statistically significant. By these results it's concluded that these specific demographic and dietary features are significant determinants for estimating the oral health status patients, based on their likelihood of developing caries.

Depression is a mental disorder characterized by recurrent sadness and loss of interest in the enjoyment of the positive aspects of life, in addition to fatigue, causing inability to perform daily activities, which leads to a loss of quality of life. To monitor depression (unipolar and bipolar patients), traditional methods rely on reports from patients, nevertheless, bias is commonly present in them. To overcome this problem, Ecological Momentary Assessment (EMA) reports have been widely used, which include data of the behavior, feelings and other types of activities recorded almost in real time through the use of portable devices and smartphones containing motion sensors. In this work a methodology was proposed to detect depressive subjects from control subjects based in the data of their motor activity, recorded by a wearable device, obtained from the &ldquo, Depresjon&rdquo, database. From the motor activity signals, the extraction of statistical features was carried out to subsequently feed a random forest classifier. Results show a sensitivity value of 0.867, referring that those subjects with presence of depression have a degree of 86.7% of being correctly classified, while the specificity shows a value of 0.919, referring that those subjects with absence of depression have a degree of 91.9% of being classified with a correct response, using the motor activity signal provided from the wearable device. Based on these results, it is concluded that the motor activity allows distinguishing between the two classes, providing a preliminary and automated tool to specialists for the diagnosis of depression.

La incidencia y prevalencia de dos enfermedades no transmisibles las cuales son diabetes mellitus y caries dentales, están en aumento, motivo principal por el que en los últimos años el estudio de la relación que hay entre estos dos padecimientos ha incrementado. Tanto la caries como la diabetes, son enfermedades multifactoriales, que tienen diversos factores de riesgo que contribuyen su inicio y progresión. Los factores de riesgo pueden clasificarse en biológicos, ambientales o socioconductuales, por mencionar algunos. La caries es problema de salud pública a nivel mundial y es conocida como la enfermedad no transmisible más extendida. La diabetes actualmente está afectando a la población mexicana en niveles preocupantes, ocupando el primer lugar en prevalencia de este padecimiento. Debido a que ambas enfermedades son prevenibles, en este trabajo se propone ell uso de una Red Nuronal Artificial que sea capaz de clasificar a los sujetos con presencia o ausencia de estas afecciones, utilizando 31 características que describen el estado del paciente. El modelo es evaluado mediante análisis estadístico tomando en cuenta la precisión, función de pérdida, el área bajo la curva (AUC por sus siglas en inglés) y la curva de características operativas receptoras (ROC por sus siglas en inglés). Se obtuvieron resultados estadísticamente significativos tieniendo una precisión de 0.99, AUC de 0.99 para las curvas ROC.

La salud oral representa un componente esencial en la calidad de vida de las personas, siendo un factor determinante en la salud general, tomando en cuenta que puede incrementar el riesgo de sufrir otros padecimientos, entre los que se encuentran algunas enfermedades crónicas. En la actualidad, las enfermedades orales son un problema de salud pública, siendo la caries dental la que presenta mayor incidencia, ocurriendo en alrededor del 90% de la población mundial, presentándose principalmente en las regiones menos desarrolladas. La alta incidencia de este padecimiento se debe a la gran cantidad de factores que la afectan, incluyendo desde procesos biológicos hasta complejas estructuras histórico-culturales y sociales, los cuales interactúan de manera simultánea, provocando que el control de esta enfermedad se vuelva altamente complicado. En este trabajo se presentan una serie de investigaciones en las que se hizo la búsqueda de la relación entre un conjunto de descriptores demográfi cos y dietéticos, obtenidos de NHANES 2013 { 2014, y la condición de caries dental. Para el desarrollo de estos trabajos fueron utilizadas diferentes técnicas de inteligencia arti ficial, tales como algoritmos de aprendizaje automático, algoritmos genéticos, redes neuronales arti ficiales y aprendizaje profundo. De acuerdo con los resultados que se presentan, es posible conocer que existe una evidente relación entre los descriptores demográ cos y dietéticos con la caries dental, principalmente entre aquellos que contienen información sobre el estado socio-económico de los sujetos, además de la edad, el género, el origen étnico y la ingesta diaria de nutrientes. Además, comparando las diferentes técnicas utilizadas en cada investigación, se puede observar que si los sujetos se separan por grupos de edad es posible seleccionar características específicas que afectan a cada grupo, permitiendo una mejor clasi cación de sujetos caso y sujetos control. Por otro lado, si la selección de características se lleva a cabo utilizando una técnica basada en un algoritmo genético, los resultados obtenidos presentan la relación de sensibilidad - especi ficidad más signi ficativa estadísticamente. En conclusión, las herramientas computacionales utilizadas en este trabajo permiten analizar información con el n de encontrar complejas relaciones entre los datos. De esta manera se presenta una serie de trabajos preliminares para el posible desarrollo de una herramienta de diagnóstico asistido por computadora, basada en determinantes demográficos y dietéticos, que podrá apoyar a los especialistas en la reducción de la alta prevalencia que presenta la condición de caries dentales, especialmente en las regiones menos desarrolladas.