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Metabolic Syndrome and Related Disorders|February 17, 2021
The Use of Machine Learning Techniques to Determine the Predictive Value of Inflammatory Biomarkers in the Development of Type 2 Diabetes MellitusRafael Garcia-Carretero, Luis Vigil-Medina, Oscar Barquero-PerezMetabolic Syndrome and Related Disorders|November 2, 2019
Relevant Features in Nonalcoholic Steatohepatitis Determined Using Machine Learning for Feature SelectionRafael Garcia-Carretero, Luis Vigil-Medina, Oscar Barquero-Perez, et al.Journal of Medical Systems|December 11, 2019
Pulse Wave Velocity and Machine Learning to Predict Cardiovascular Outcomes in Prediabetic and Diabetic PopulationsRafael Garcia-Carretero, Luis Vigil-Medina, Oscar Barquero-Perez, et al.Medical & Biological Engineering & Computing|February 27, 2020
Use of a K-nearest neighbors model to predict the development of type 2 diabetes within 2 years in an obese, hypertensive populationRafael Garcia-Carretero, Luis Vigil-Medina, Inmaculada Mora-Jimenez, et al.Informatics for Health & Social Care|April 1, 2021
Machine learning approaches to constructing predictive models of vitamin D deficiency in a hypertensive population: a comparative studyRafael Garcia Carretero, Luis Vigil-Medina, Oscar Barquero-Perez, et al.Metabolic Syndrome and Related Disorders|January 14, 2020
Logistic LASSO and Elastic Net to Characterize Vitamin D Deficiency in a Hypertensive Obese PopulationRafael Garcia-Carretero, Luis Vigil-Medina, Oscar Barquero-Perez, et al.Diabetes & Metabolic Syndrome|April 18, 2018
Cardiovascular risk assessment in prediabetic patients in a hypertensive population: The role of cystatin CRafael Garcia-Carretero, Luis Vigil-Medina, Inmaculada Mora-Jimenez, et al.Pageof 1