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dc.contributor.authorVelásquez, Jesús
dc.contributor.authorSevereyn, Erika
dc.contributor.authorHerrera, Hector Antonio
dc.contributor.authorUtrera, Nestor
dc.contributor.authorEncalada Torres, Lorena Esperanza
dc.contributor.authorWong de balzan , Sara Null
dc.date.accessioned2019-07-26T14:18:40Z-
dc.date.available2019-07-26T14:18:40Z-
dc.date.issued2018
dc.identifier.isbn978-153866657-9
dc.identifier.issn0000-0000
dc.identifier.urihttps://www.scopus.com/inward/record.uri?partnerID=HzOxMe3b&scp=85060711823&origin=inward
dc.descriptionDiabetes mellitus and cardiovascular diseases are ailments related to insulin resistance (IR) and obesity. The existing methods for IR and obesity diagnosis are the HOMA-IR and body mass index (BMI), respectively. Those methods have limitations in the diagnosis. The aim of this research is to propose dimensionless indexes that can diagnose subjects with IR and obesity using anthropometrics and biochemical variables (waist circumference, height, weight, systolic blood pressure, diastolic blood pressure and triglycerides). Three dimensionless indexes, designed from the π Vaschy-Buckingham theorem, were assessed using receiver operating characteristic (ROC) curves and a database of 829 subjects. The index π 1 , constructed with the variables: waist circumference, triglycerides height and weight; obtained a better performance as classifier of IR and obesity, presenting an area under the ROC curve of 0.78, sensitivity of 0.82 and specificity of 0.68 for IR diagnosis, and an area under the ROC curve of 0.68, sensitivity of 0.66 and specificity of 0.65 for obesity diagnosis. The π 1 dimensionless index designed in this study is a simple method that allows diagnosing two pathologies from variables that can be collected in a routine medical examination.
dc.description.abstractDiabetes mellitus and cardiovascular diseases are ailments related to insulin resistance (IR) and obesity. The existing methods for IR and obesity diagnosis are the HOMA-IR and body mass index (BMI), respectively. Those methods have limitations in the diagnosis. The aim of this research is to propose dimensionless indexes that can diagnose subjects with IR and obesity using anthropometrics and biochemical variables (waist circumference, height, weight, systolic blood pressure, diastolic blood pressure and triglycerides). Three dimensionless indexes, designed from the π Vaschy-Buckingham theorem, were assessed using receiver operating characteristic (ROC) curves and a database of 829 subjects. The index π 1 , constructed with the variables: waist circumference, triglycerides height and weight; obtained a better performance as classifier of IR and obesity, presenting an area under the ROC curve of 0.78, sensitivity of 0.82 and specificity of 0.68 for IR diagnosis, and an area under the ROC curve of 0.68, sensitivity of 0.66 and specificity of 0.65 for obesity diagnosis. The π 1 dimensionless index designed in this study is a simple method that allows diagnosing two pathologies from variables that can be collected in a routine medical examination.
dc.language.isoes_ES
dc.publisherInstitute of Electrical and Electronics Engineers Inc.
dc.source2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018. Proceeding
dc.subjectBmi
dc.subjectDimensional Analysis
dc.subjectHoma-Ir
dc.subjectInsulin Resistance
dc.subjectObesity
dc.titleTowards the insulin resistance and obesity diagnosis: A dimensional analysis approach
dc.title.alternative
dc.typeARTÍCULO DE CONFERENCIA
dc.description.cityCuenca
dc.ucuenca.idautorSgrp-901-1
dc.ucuenca.idautorSgrp-901-2
dc.ucuenca.idautorSgrp-901-3
dc.ucuenca.idautorSgrp-901-4
dc.ucuenca.idautor0102905353
dc.ucuenca.idautor081929618
dc.identifier.doi10.1109/ETCM.2018.8580313
dc.ucuenca.embargoend2050-12-31
dc.ucuenca.versionVersión publicada
dc.ucuenca.embargointerno2050-12-31
dc.ucuenca.areaconocimientounescoamplio09 - Salud y Bienestar
dc.ucuenca.afiliacionVelásquez, J., Universidad Simón Bolívar, Barranquilla, Colombia
dc.ucuenca.afiliacionSevereyn, E., Universidad Simón Bolívar, Barranquilla, Colombia
dc.ucuenca.afiliacionHerrera, H., Universidad Simón Bolívar, Barranquilla, Colombia
dc.ucuenca.afiliacionUtrera, N., Universidad Simón Bolívar, Barranquilla, Colombia
dc.ucuenca.afiliacionEncalada, L., Universidad de Cuenca, Facultad de Ciencias Médicas, Cuenca, Ecuador
dc.ucuenca.afiliacionWong de balzan, S., Universidad de Cuenca, Departamento de Ingeniería Eléctrica, Electrónica y Telecomunicaciones(DEET), Cuenca, Ecuador; Wong de balzan, S., Universidad de Cuenca, Facultad de Ingeniería, Cuenca, Ecuador
dc.ucuenca.volumenvolumen 00
dc.ucuenca.indicebibliograficoSCOPUS
dc.ucuenca.factorimpacto0.138
dc.ucuenca.numerocitaciones0
dc.ucuenca.areaconocimientofrascatiamplio3. Ciencias Médicas y de la Salud
dc.ucuenca.paisECUADOR
dc.ucuenca.conferencia2018 IEEE 3rd Ecuador Technical Chapters Meeting, ETCM 2018
dc.ucuenca.areaconocimientofrascatiespecifico3.2 Medicina Clínica
dc.ucuenca.areaconocimientofrascatidetallado3.2.20 Endocrinología y Metabolismo
dc.ucuenca.areaconocimientounescoespecifico091 - Salud
dc.ucuenca.areaconocimientounescodetallado0914 - Tecnologías de Diagnóstico y Tratamiento Médico
dc.ucuenca.fechainicioconferencia2018-10-15
dc.ucuenca.fechafinconferencia2018-10-19
dc.ucuenca.organizadorconferenciaDiego Benítez, IEEE Ecuador Section
dc.ucuenca.comiteorganizadorconferenciaMónica Huerta, Universidad Politécnica Salesiana (UPS); Enrique V. Carrera, Universidad de las Fuerzas Armadas (ESPE)
dc.ucuenca.urifuentehttps://ieeexplore.ieee.org/xpl/conhome/8561378/proceeding
dc.contributor.ponenteVelásquez, Jesús
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