ArticleScientific reports2021
Ranking of a wide multidomain set of predictor variables of children obesity by machine learning variable importance techniques.
Article in Scientific reports, 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 19 papers.
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Who cites it
19 citing papers in PubMed.
- Psychological and Behavioral Predictors of Weight Loss in the Reach Ahead for Lifestyle and Health-Diabetes Lifestyle Intervention Cohort.Journal of the Academy of Nutrition and Dietetics · 2023Trial
- Multimodal (Bio)Markers and Risk of Obesity - A Comprehensive Scoping Review.Advances in nutrition (Bethesda, Md.) · 2026Article
- Interpretable machine learning for identifying adolescent obesity risk and identifying key determinants.Frontiers in public health · 2026Article
- Development and validation of an explainable machine learning-based risk prediction model for obesity in Chinese children and adolescents: a population-based study.Frontiers in nutrition · 2026Article
- Per- and polyfluoroalkyl substances mixture exposure is associated with colorectal cancer TNM stage.Frontiers in toxicology · 2026Article
- AI-based Assessment of Risk Factors for Coronary Heart Disease in Patients With Diabetes Mellitus and Construction of a Prediction Model for a Treatment Regimen.Reviews in cardiovascular medicine · 2025Article
- Pathways to chronic disease detection and prediction: Mapping the potential of machine learning to the pathophysiological processes while navigating ethical challenges.Chronic diseases and translational medicine · 2025Review
- Predicting the onset of overweight in Chinese high school students: a machine-learning approach in a one-year prospective cohort study.Endocrine · 2024Article
- Prediction and classification of obesity risk based on a hybrid metaheuristic machine learning approach.Frontiers in big data · 2024Article
- Classification of Obesity among South African Female Adolescents: Comparative Analysis of Logistic Regression and Random Forest Algorithms.International journal of environmental research and public health · 2023Article
- Effects of machine learning-based clinical decision support systems on decision-making, care delivery, and patient outcomes: a scoping review.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Prospective Association Between Video and Computer Game Use During Adolescence and Incidence of Metabolic Health Risks: Secondary Data Analysis.JMIR pediatrics and parenting · 2023Article
- Predicting Childhood Obesity Based on Single and Multiple Well-Child Visit Data Using Machine Learning Classifiers.Sensors (Basel, Switzerland) · 2023Article
- Machine learning identifies prominent factors associated with cardiovascular disease: findings from two million adults in the Kashgar Prospective Cohort Study (KPCS).Global health research and policy · 2022Article
- Machine learning insights concerning inflammatory and liver-related risk comorbidities in non-communicable and viral diseases.World journal of gastroenterology · 2022Review
- Folliculin-interacting protein FNIP2 impacts on overweight and obesity through a polymorphism in a conserved 3' untranslated region.Genome biology · 2022Article
- A Machine Learning Approach to Identify Predictors of Frequent Vaping and Vulnerable Californian Youth Subgroups.Nicotine & tobacco research : official journal of the Society for Research on Nicotine and Tobacco · 2022Article
- Predictors of perceived success in quitting smoking by vaping: A machine learning approach.PloS one · 2022Article
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Corrections and comments
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Authors and funding
6 authors.
Funding
No grant is acknowledged in the PubMed record.
Abstract
The increased prevalence of childhood obesity is expected to translate in the near future into a concomitant soaring of multiple cardio-metabolic diseases. Obesity has a complex, multifactorial etiology, that includes multiple and multidomain potential risk factors: genetics, dietary and physical activity habits, socio-economic environment, lifestyle, etc. In addition, all these factors are expected to exert their influence through a specific and especially convoluted way during childhood, given the fast growth along this period. Machine Learning methods are the appropriate tools to model this complexity, given their ability to cope with high-dimensional, non-linear data. Here, we have analyzed by Machine Learning a sample of 221 children (6-9 years) from Madrid, Spain. Both Random Forest and Gradient Boosting Machine models have been derived to predict the body mass index from a wide set of 190 multidomain variables (including age, sex, genetic polymorphisms, lifestyle, socio-economic, diet, exercise, and gestation ones). A consensus relative importance of the predictors has been estimated through variable importance measures, implemented robustly through an iterative process that included permutation and multiple imputation. We expect this analysis will help to shed light on the most important variables associated to childhood obesity, in order to choose better treatments for its prevention.
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