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

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.

Predicting Overweight and Obesity Status Among Malaysian Working Adults With Machine Learning or Logistic Regression: Retrospective Comparison Study

“…A wide range of ML-based algorithms incorporating various predictors and risk factors, training set sizes, and degrees of implementation have been used to predict adult obesity [11,14]. The reported accuracy of ML algorithms to predict adult obesity as a binary outcome ranges broadly, from 0.59 to 0.97 for overall accuracy [15][16][17][18][19][20][21][22][23][24] and 0.51 to 0.99 for the area under the curve (AUC) [15,19,20,23,24]. A review suggested that ML-based models predicted childhood and adolescent obesity much better than linear regression [13].…”

Artificial intelligence and Psychiatry: An overview

“…Machine learning (ML) algorithms have been used to forecast BMI values based on psychological variables, like depression, with an accuracy of over 80% ( Delnevo et al, 2021 ).…”

Applications of Artificial Intelligence to Obesity Research: Scoping Review of Methodologies

“…Therefore, of the 3090 articles identified initially through the keyword search, 46 (1.49%) were included in the review . [28,32,33,37,42,46,48,[50][51][52][53]57,58,60,62,63]; 6 (13%) in China [39,40,45,56,64,65]; 3 (7%) each in the United Kingdom [27,68,69] and Korea [35,43,49]; 2 (4%) each in Italy [36,71], Turkey [41,70], Finland [44,59], Germany [54,55], and India [36,71]; and 1 (2%) each in Saudi Arabia [26], Iran [67], Serbia [66], Portugal [61], Spain [47], Singapore [38], Australia [34], and Indonesia [29]. Of the 46 studies, 32 (70%) adopted a cross-sectional study design [26,…”

“…Of the 46 studies, 23 (50%) focused on adults, 14 (30%) on children and adolescents, 1 (2%) on people of all ages, and the remaining 8 (17%) did not report the age range of participants. [26][27][28][29][33][34][35][36][37]39,41,[44][45][46][47][49][50][51][53][54][55][56][58][59][60][61][62][63][64][65][66][67][68]70], 8 (17%) analyzed digital image data [30,31,38,40,42,43,57,71], and 4 (9%) analyzed text data [32,48,52,69]. Obesity-related measures used across the studies included anthropometrics (eg, body weight, BMI, BFP, WC, and WHR) and biomarkers.…”

“…Second, most (18/22, 82%) of the studies comparing AI models with conventional statistical methods reported that the AI models achieved higher prediction accuracy on test data, whereas others (4/22, 18%) found similar model performances; for example, ML and DL models were found to explain a larger proportion of variations in county-level obesity prevalence than conventional statistical approaches [50]. ML models showed flexibility in handling various variable types [36,41] and large-scale data sets [32] and producing robust, generalizable inferences [41,54,64,65] with higher prediction accuracy [61,66]. By contrast, Cheng et al [37] reported that ML algorithms and conventional statistical approaches had similar performance.…”