Statistical models to predict incident diabetes are often based on limited variables. Here we pursued two main goals: 1) investigate the relative performance of a machine learning method such as Random Forests (RF) for detecting incident diabetes in ...
Nutrition (Burbank, Los Angeles County, Calif.)
Mar 16, 2016
OBJECTIVE: Increasing evidence has suggested a protective role of vitamin D on metabolic syndrome (MetS). However, studies addressing this issue are limited in Asia and it remains unclear whether calcium could modify the association. We examined the ...
Clinical and experimental hypertension (New York, N.Y. : 1993)
Dec 1, 2025
OBJECTIVES: Sufficient attention has not been given to machine learning (ML) models using longitudinal data for investigating important predictors of new onset of hypertension. We investigated the predictive ability of several ML models for the devel...
PURPOSE: Insulin resistance (IR) is a condition closely associated with cardiovascular risk factors and metabolic dysfunction-associated steatotic liver disease (MASLD) is emerging as a significant IR-related complication. We aimed to develop a predi...
BACKGROUND: Physical characteristics including body size and configuration, are considered as one of the key influences on the optimum performance in athletes. Despite several analyzing methods for modeling the slimming estimation in terms of reducti...
OBJECTIVES: The neuropeptide hormone- Oxytocin (OXT) and glycoprotein Lipocalin-2 (LCN-2) are strongly associated with cardiometabolic risks of insulin resistance in metabolic syndrome (MetS) and prediabetes (preDM).
OBJECTIVE: Adiponectin is the major product of adipose tissue. The aim of this study was to associate adiponectin levels with adipose tissue and metabolic indices.
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