The Prediction of Body Mass Index from Negative Affectivity through Machine Learning: A Confirmatory Study release_gvlpqhlxobcjrpgodrilmoirce

by Giovanni Delnevo, Giacomo Mancini, MARCO ROCCETTI, Paola Salomoni, Elena Trombini, Federica Andrei

Published in Sensors by MDPI AG.

2021   Volume 21, Issue 7, p2361

Abstract

This study investigates on the relationship between affect-related psychological variables and Body Mass Index (BMI). We have utilized a novel method based on machine learning (ML) algorithms that forecast unobserved BMI values based on psychological variables, like depression, as predictors. We have employed various machine learning algorithms, including gradient boosting and random forest, with psychological variables relative to 221 subjects to predict both the BMI values and the BMI status (normal, overweight, and obese) of those subjects. We have found that the psychological variables in use allow one to predict both the BMI values (with a mean absolute error of 5.27–5.50) and the BMI status with an accuracy of over 80% (metric: F1-score). Further, our study has also confirmed the particular efficacy of psychological variables of negative type, such as depression for example, compared to positive ones, to achieve excellent predictive BMI values.
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Type  article-journal
Stage   published
Date   2021-03-29
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DOI  10.3390/s21072361
PubMed  33805257
PMC  PMC8037317
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