Novel bioparameters derived from bioimpedance measurements for accurate prediction of weight status in infant-juvenile individuals: A regression analysis.

Abstract

In this study, a linear support vector machine regression model was used to explore the correlation between weight status and two novel bioparameters, specific resistance and reactance, in an infant-juvenile cohort from eastern Cuba. The model was trained using various characteristics, including bioimpedance measurements, to predict phase angle, specific resistance, and reactance with high accuracy. The results showed that the variation of these characteristics with weight status and sex is consistent with previous literature. Additionally, two robust bioparameters derived from bioimpedance measurements and anthropometric-physiological parameters were identified for predicting weight status. The predictive models developed in this study are essential for accurately assessing weight status and disease risks in infants and juveniles in the eastern Cuban region. These findings highlight the potential applications of bioimpedance measurements and bioparameters in health and disease risk assessment, contributing to the growing body of literature on this topic.