Título:	Assessment of machine learning model performance for clinical prediction of insulin resistance in the study of cardiovascular risk in adolescents - ERICA
Autor(es):	Silva, Jéssica Aparecida Bloch, Katia Vergetti Szklo, Moyses Deusdará, Rodolfo
ORCID:	https://orcid.org/0000-0002-6992-3159 https://orcid.org/0000-0002-1343-3265
Afiliação do autor:	University of Brasília, Postgraduate Program in Medical Sciences, Faculty of Medicine University of Rio de Janeiro, Institute for Studies in Public Health Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore University of Brasília, Postgraduate Program in Medical Sciences University of Brasília, Faculty of Medicine National Institute of Science and Technology for Health Technology Assessment (IATS) University of Brasília, Laboratory of Epidemiology, Faculty of Medicine Institute for Health Assessment and Translation for Chronic and Neglected Diseases of High Relevance (IATS-CARE), Belo Horizonte
Assunto:	Insulina Diabetes Saúde
Data de publicação:	15-Mar-2026
Editora:	MDPI
Referência:	SILVA, Jéssica Aparecida et al. Assessment of machine learning model performance for clinical prediction of insulin resistance in the study of cardiovascular risk in adolescents—ERICA. Journal of Clinical Medicine, [S.l.], v. 15, n. 6, p. 2224, 2026. DOI: https://doi.org/10.3390/jcm15062224. Disponível em: https://www.mdpi.com/2077-0383/15/6/2224. Acesso em: 18 jun.2026.
Abstract:	Background: Insulin resistance is defined as reduced tissue responsiveness to insulin-mediated glucose actions. Gold standard methods like hyperinsulinemic-euglycemic clamp and hyperglycemic clamps are costly and rarely used in large epidemiological studies. The aim was to evaluate the best performing machine learning algorithm for insulin resistance predictions in Brazilian adolescents. Methods: We used data from 37,454 Brazilian adolescents from 12 to 17 years, sampled from the Study of Cardiovascular Risk Factors in Adolescents (2013–2014). Covariates included other cardiovascular risk factors. We evaluate seven machine learning models stratifying the subset by sex. The performance of the models was assessed by area under the curve (AUC), calibration curves and decision curve analysis (DCA). Finally, we adopted the SHAP approach to assess the importance of each variable to the best performing ML model. Results: The Logistic Regression model presented the best AUC value (AUC = 0.8 for boys and girls). The best performing ML models had higher calibration in girls than in boys. The DCA curves showed prevalence of almost equal values for girls and for boys. The most important clinical predictors for both sexes were waist circumference, triglycerides and age. Conclusions: Logistic Regression proved to be the best clinical prediction model comparable to complex models. Further studies are needed in more diverse populations.
Unidade Acadêmica:	Faculdade de Medicina (FM)
Programa de pós-graduação:	Programa de Pós-Graduação em Ciências Médicas
Licença:	Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license.
DOI:	https://doi.org/10.3390/jcm15062224
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