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

Giovanni Delnevo, Giacomo Mancini, Marco Roccetti, Paola Salomoni, Elena Trombini, Federica Andrei
2021 Sensors  
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
more » ... 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.
doi:10.3390/s21072361 pmid:33805257 pmcid:PMC8037317 fatcat:gvlpqhlxobcjrpgodrilmoirce