Fat mass prediction equations and reference ranges for Saudi Arabian Children aged 8–12 years using machine technique method

Abstract

Background The number of children with obesity has increased in Saudi Arabia, which is a significant public health concern. Early diagnosis of childhood obesity and screening of the prevalence is needed using a simple in situ method. This study aims to generate statistical equations to predict body fat percentage (BF%) for Saudi children by employing machine learning technology and to establish gender and age-specific body fat reference range. Methods Data was combined from two cross-sectional studies conducted in Saudi Arabia for 1,292 boys and girls aged 8–12 years. Body fat was measured in both studies using bio-electrical impedance analysis devices. Height and weight were measured and body mass index was calculated and classified according to CDC 2,000 charts. A total of 603 girls and 374 boys were randomly selected for the learning phase, and 153 girls and 93 boys were employed in the validation set. Analyses of different machine learning methods showed that an accurate, sensitive model could be created. Two regression models were trained and fitted with the construction samples and validated. Gradient boosting algorithm was employed to achieve a better estimation and produce the equations, then the root means squared error (RMSE) equation was performed to decrease the error. Body fat reference ranges were derived for children aged 8–12 years. Results For the gradient boosting models, the predicted fat percentage values were more aligned with the true value than those in regression models. Gradient boosting achieved better performance than the regression equation as it combined multiple simple models into a single composite model to take advantage of that weak classifier. The developed predictive model archived RMSE of 3.12 for girls and 2.48 boys. BF% and Fat mass index charts were presented in which cut-offs for 5th, 75th and 95th centiles are used to define ‘under-fat’, ‘normal’, ‘overfat’ and ‘subject with obesity’. Conclusion Machine learning models could represent a significant advancement for investigators studying adiposity-related issues in children. These models and newly developed centile charts could be useful tools for the estimation and classification of BF%.

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.…”

“…Pang et al [33] used XGBoost to predict early childhood obesity based on electronic health records. Alkutbe et al [27] applied gradient boosting to predict BFP based on cross-sectional health survey data collected in Saudi Arabia.…”

“…Recent advances in AI showcase the feasibility and possibly superior performance of multimodal, multitask ML and DL models that are trained on diverse data types (eg, tabular plus text, image, video, or audio) and can handle many domains of downstream tasks (eg, text generation, object detection, time series prediction, and speech recognition) simultaneously [138][139][140]. However, it should be noted that the predictive accuracy of AI models may vary across gender and age groups [27] and sex and age groups [59]. Different from BMI, BMI z scores adjust for sex and age differences [141].…”

Total Body Fat Assessed by Bioelectrical Impedance Analysis among University Students

“…DXA is considered as the gold standard method to measure body composition (Scafoglieri and Clarys 2018). Body fat percentage assessed by BIA was highly correlated to those determined by DXA or magnetic resonance imaging with r values ranged between 0.92 and 0.96 (Bosy-Westphal et al 2008) In Saudi Arabia, few recent studies have used BIA scales for the estimation and classification of body fat percentage in children (Alkutbe et al 2021;Shaikh et al 2016) and in university students (Alahmadi 2021;Albaker et al 2021). However, there are different BIA devices to measure body fat percentage such as Seca, Inbody, Tanita, and Omron (Bosy-Westphal et al 2008).…”