Examinando por Autor "Wong, Sara"
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- PublicaciónSólo datosAnalysis of Anthropometric Measurements Using Receiver Operating Characteristic Curve for Impaired Waist to Height Ratio Detection(Advances in Intelligent Systems and Computing, 2020-12-19) Severeyn, Erika; La Cruz, Alexandra; Wong, Sara; Perpiñan, GilbertoMetabolic dysfunctions such as obesity, insulin resistance, metabolic syndrome, and glucose tolerance are strongly related to each other. The presence of any of them in a person translates into a high risk of diseases such as diabetes, heart failure, and cardiovascular disease. Anthropometric measurements such as body circumferences, body folds, and anthropometric indices such as waist-height ratio (WHtR) and body mass index (BMI) have been widely used in the study of metabolic diseases. This study aims to look for relationships between WHtR and anthropometric measurements such as subcutaneous folds and body circumferences. For this purpose, a database of 1863 subjects was used, 16 anthropometric variables were measured for each participant in the database and the BMI was calculated. The receiver operating characteristic (ROC) curves were used to assess the ability of BMI and each anthropometric measurement was used to diagnose BMI impairment. The findings reported in this research strongly suggest that the diagnosis of WHtR deficiency can be made from circumferences, skinfolds, and BMI. In this study, the anthropometric measures that best detect subjects with WHtR deficiency were BMI, subscapular skinfold, supra iliac skinfold, and arm circumference with a high probability of detecting normal WHtR-deficient subjects. Abdominal circumference is one of the areas that have the most direct relationship with cardiac metabolic risk, however, the findings of this study open the possibility of studying accumulated fat tissue in the arms and back as areas that could also indicate a risk of metabolic dysfunction.
- PublicaciónSólo datosAnalysis of Receiver Operating Characteristic Curve Using Anthropometric Measurements for Obesity Diagnosis(Advances in Intelligent Systems and Computing, 2020-07-27) Severeyn, Erika; Velásquez, Jesús; Herrera, Héctor; Wong, Sara; La Cruz, AlexandraToday, obesity is a major public health problem. Obesity increases the risk of diabetes, coronary artery disease, stroke, cancer, premature death and contributes substantially the costs to society. Obesity can be diagnosed with body mass index (BMI). According to the World Health Organization, the diagnosis of overweight is made with a BMI ≥ 25Kg/m2, and obesity with a BMI ≥ 30kg/m2 . The diagnosis of obesity has been made using the abdominal circumference, the hip circumference, the thickness of the skin folds and the percentage of body fat (measured directly or indirectly). Besides, the characteristic operating receiver curves (ROC) have been used to find the optimal cut- off points of hip and waist circumference for the diagnosis of obesity. The aim of this study is to evaluate the ability of anthropometric measures for diagnosing overweight and obesity. A database of 1053 subjects with 26 anthropometric measurements was used. For evaluating the predictive ability of anthropometric measures, the area under the ROC curve (AUCROC ), the sensitivity (SEN), the specificity (SP E), the negative predictive value (NP V ) and the positive predictive value (P P V ) were calculated. The hip circumference was the anthropometric value that best detected overweight/obese subjects with a AUCROC = 0.932 (SEN = 0.871, SP E = 0.855, P P V = 0.536 and NP V = 0.972) and an optimal cut-off point of 97.2cm for recognition of obesity. The findings reported in this research suggest that the diagnosis of obesity can be made with anthropometric measurements. In the future, machine learning techniques, such as: k-means, neural networks or support vector machines; will be explored for the detection of overweight and obesity.
- PublicaciónSólo datosAssessment of Anthropometric Measurements for Obesity and Abnormal Body Fat Percentage Diagnosis Using k-means as Clustering Technique(Communications in Computer and Information Science, 2020-11-25) La Cruz, Alexandra; Severeyn, Erika; Velásquez, Jesús; Herrera, Héctor; Wong, SaraThe increased prevalence of overweight and obesity has become a major factor in public spending in countries around the world. The diagnosis of overweight and obesity is based on body mass index (BMI) and body fat percentage (BFP). The World Health Organization proposed BMI cut-off points to define overweight and obesity. Recently epidemiological studies established as normal BFP a BFP < 25 for men and BFP < 30 for women. A high correlation between a high BMI, abnormal BFP and skin thinness have been found in numerous studies. The aim of this work is to evaluate the k-means clustering algorithm using anthropometric measurements for the classification of subjects with overweight/obesity and abnormal BFP. Precision (P), accuracy (Acc) and recall (R) were calculated to evaluate the efficiency of the method to classify overweight/obesity and abnormal BFP. Results of this research suggest that the k-means method applied to anthropometric measurements can make an acceptable classification of overweight/obesity and abnormal BFP. The arm circumferences values show the best Acc, P and R (0.79, 0.84 and 0.71) compared to all other measurements for overweight/obesity diagnosis, otherwise, suprailiac and abdominal skinfolds values show the best Acc, P and R (0.73, 0.73 and 0.64) compared to all other measurements for abnormal BFP diagnosis. Results that are supported by studies asserting a strong relationship between arm circumferences, abdominal skinfold, suprailiac skinfold, BFP and BMI. Other machine learning techniques, such as neural networks and the support vector machine, will be studied in the future to assess the relationship between BMI, BFP and anthropometric measurements.
- PublicaciónSólo datosClassification of Impaired Waist to Height Ratio Using Machine Learning Technique(Advances in Intelligent Systems and Computing, 2021-01-04) La Cruz, Alexandra; Severeyn, Erika; Wong, Sara; Perpiñan, GilbertoMetabolic dysfunctions are a set of metabolic risk factors that include abdominal obesity, dyslipidemia, insulin resistance, among others. Individuals with any of these metabolic dysfunctions are at high risk of developing type 2 diabetes and cardiovascular disease. Several parameters and anthropometric indices are used to detect metabolic dysfunctions, such as waist circumference and waist-height ratio (WHtR). The WHtR has an advantage over the body mass index (BMI) since the WHtR provides information on the distribution of body fat, particularly abdominal fat. Central fat distribution is associated with more significant cardio-metabolic health risks than total body fat. Machine learning techniques involve algorithms capable of predicting and analyzing data, increasing our understanding of the events being studied. k-means is a clustering algorithm that has been used in the detection of obesity. This research aims to apply the k-means grouping algorithm to study its capability as an impaired WHtR classifier. Accuracy (Acc), recall (Rec), and precision (P) were calculated. A database of 1863 subjects was used; the database consists of fifteen (15) anthropometric variables and two (2) indices; each anthropometric variable was measured for each participant. The results reported in this research suggest that the k-means clustering algorithm is an acceptable classifier of impaired WHtR subjects ( Acc=0.81 , P=0.83 , and Rec=0.73 ). Besides, the k-means algorithm was able to detect subjects with overweight and fatty tissue deposits in the back and arm areas, suggesting that fat accumulation in these areas is directly related to abdominal fat accumulation.
- PublicaciónSólo datosPhysical Activity Classification Using an Artificial Neural Networks Based on the Analysis of Anthropometric Measurements(Advances in Intelligent Systems and Computing, 2020-10-11) Alvarez, Antonio J.; Severeyn, Erika; Wong, Sara; Herrera, Héctor; Velásquez, Jesús; La Cruz, AlexandraPhysical activity is one of the most important factors in leading a healthy life, which has increased the interest in the scientific community to evaluate methods and tools that can help people maintain an exercise routine, such as portable devices that can track the movements of the user and provide an appropriate feedback. Interest has also emerged in assessing the discrimination between physically active and inactive persons through the use of readily available data, which is the aim of this work. In this case, we used an auto-encoder to find the most outstanding characteristics of an anthropometric data set, in order to get the most representative attributes. Then use them to train an Artificial Neural Network (ANN), so that it could learn to identify between a physically active and a sedentary person. The ANN obtained 81% accuracy, 82% precision, 88% recall, 83% F1 score and 0.89 AUC. These results position the ANN as a viable model that could be used as a tool in scenarios such as customer profiling for different interested companies.
- PublicaciónSólo datosStudy of Basic Reproduction Number Projection of SARS-CoV-2 Epidemic in USA and Brazil(2020 IEEE ANDESCON, 2021-01-30) Severeyn, Erika; Wong, Sara; Herrera, Héctor; La Cruz, Alexandra; Velázquez, JesúsIn December 2019, a group of patients presented a diagnosis of pneumonia of unknown etiology in Hubei Province, Wuhan, China. By January 2020, authorities around the world faced a new coronavirus (SARS-CoV-2). By August 2020, the two countries with the highest number of SARS-CoV-2 infections are the USA and Brazil. The transmission rate of a virus is studied from the basic reproduction number (R0). The SIR model is the simplest compartmental epidemiological model (Susceptible, Infectious and Recovered). The SIR model can be used to estimate R0 by fitting the curve of the infected compartment to the experimental curve of infected subjects per day. The aim of this work is to study the projection of the R0 of SARS-CoV-2 in the USA and Brazil. For this purpose, five experiments were performed by adjusting the SIR model curve of infected compartment to experimental data at five time intervals (the first 14, 28, 42, 56 and 187 days for the USA data, and 177 days for Brazil data). In the first two time intervals the R0 varied between 5.46 and 7.75 for the USA data and 1.84 and 4.29 for Brazil data, and in the last three time intervals the R0 decreased to 1.05 for the USA data and 1.01 for Brazil data, suggesting that the social distancing measures implemented in both countries were able to decrease the infection spreading. The differences in the R0 values of the five experiments imply that R0 also depends on the preventive measures implemented to face the pandemic.