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Open AccessFeature PaperArticle

Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms

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Cátedras CONACYT Consejo Nacional de Ciencia y Tecnología, Ciudad de México 08400, Mexico
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Instituto Nacional de Cardiología Ignacio Chávez, Ciudad de México 14080, Mexico
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División Académica de Ciencias y Tecnologías de la Información, Universidad Juárez Autónoma de Tabasco, Cunduacán, Tabasco 86690, Mexico
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Authors to whom correspondence should be addressed.
Symmetry 2020, 12(4), 581; https://doi.org/10.3390/sym12040581
Received: 15 March 2020 / Revised: 26 March 2020 / Accepted: 26 March 2020 / Published: 7 April 2020
Metabolic syndrome is a health condition that increases the risk of heart diseases, diabetes, and stroke. The prognostic variables that identify this syndrome have already been defined by the World Health Organization (WHO), the National Cholesterol Education Program Third Adult Treatment Panel (ATP III) as well as by the International Diabetes Federation. According to these guides, there is some symmetry among anthropometric prognostic variables to classify abdominal obesity in people with metabolic syndrome. However, some appear to be more sensitive than others, nevertheless, these proposed definitions have failed to appropriately classify a specific population or ethnic group. In this work, we used the ATP III criteria as the framework with the purpose to rank the health parameters (clinical and anthropometric measurements, lifestyle data, and blood tests) from a data set of 2942 participants of Mexico City Tlalpan 2020 cohort, applying machine learning algorithms. We aimed to find the most appropriate prognostic variables to classify Mexicans with metabolic syndrome. The criteria of sensitivity, specificity, and balanced accuracy were used for validation. The ATP III using Waist-to-Height-Ratio (WHtR) as an anthropometric index for the diagnosis of abdominal obesity achieved better performance in classification than waist or body mass index. Further work is needed to assess its precision as a classification tool for Metabolic Syndrome in a Mexican population. View Full-Text
Keywords: metabolic syndrome; Random Forest; Youden Index; Mexico City; cohort study; waist to height ratio metabolic syndrome; Random Forest; Youden Index; Mexico City; cohort study; waist to height ratio
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Gutiérrez-Esparza, G.O.; Infante Vázquez, O.; Vallejo, M.; Hernández-Torruco, J. Prediction of Metabolic Syndrome in a Mexican Population Applying Machine Learning Algorithms. Symmetry 2020, 12, 581.

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