The role of various physiological and bioelectrical parameters for estimating the weight status in infants and juveniles cohort from the Southern Cuba region: a machine learning study

Abstract

Objective: The search for other indicators to assess the weight status of individuals is important as it may provide more accurate information and assist in personalized medicine.This work is aimed to develop a machine learning predictions of weigh status derived from bioimpedance measurements and other physical parameters of healthy infant juvenile cohort from the Southern Cuba Region, Santiago de Cuba.

Methods: The volunteers were selected between 2002 and 2008, ranging in age between 2 and 18 years old. In total, 393 female and male infant and juvenile individuals are studied. The bioimpedance parameters are obtained by measuring standard tetrapolar whole-body configuration. A classification model are performed, followed by a prediction of other bioparameters influencing the weight status.

Results: The results obtained from the classification model indicate that fat-free mass, reactance, and corrected resistance primarily influence the weight status of the studied population. Specifically, the regression model demonstrates that other bioparameters derived from impedance measurements can be highly accurate in estimating weight status.

Conclusion: The classification and regression predictive models developed in this work are of the great importance for accessing to the weigh status with high accuracy of younger individuals at the Oncological Hospital in Santiago de Cuba, Cuba.

Keywords: Bioimpedance; Biomedical parameters; Body mass index; Fat-free mass; Machine learning.

Fig. 1

Receiver operating characteristic curve (ROC) for each positive class considered: a), b) and c) corresponds to the complete cohort, d), e) and f) to 2–11, while g), h) and i) to 12–18 age group, respectively

Fig. 2

Confusion matrix for (a) 2–18, (b) 2–11 and (c) 12–18 age group, respectively

Fig. 3

Feature importance of: (a) 2 to 18, (b) 2 to 11 and (c) 12 to 18 age group, respectively. The highest bar represents the most important characteristic of the classification model

Fig. 4

Response vs. predicted plot of the selected responses of each cohort: (a) 2–18, (b) 2–11 and (c) 12–18 age groups

Fig. 5

True and predicted anthropometric and bioelectrical parameters by weight status of each: (a) 2–18, (b) 2–11 and (c) 12–18 age group

Fig. 6

True and predicted anthropometric and bioelectrical parameters by gender class of: (a) 2–18, (b) 2–11 and (c) 12–18 age group