ArticleApplied clinical informatics2015
Machine Learning Techniques for Prediction of Early Childhood Obesity.
Article in Applied clinical informatics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 68 papers, 2 of them syntheses that pooled it.
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Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
68 citing papers in PubMed, 2 syntheses or guidelines pooled it.
- Systematic Review of Machine Learning applied to the Prediction of Obesity and Overweight.Journal of medical systems · 2023Pooled it
- Pooled it
- Harnessing artificial intelligence for pediatric health: Current trends and future opportunities.iScience · 2026Review
- A Maturation-Aware Machine Learning Framework for Screening the Nutritional Status of Adolescents.Nutrients · 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
- Study of the Genetic Basis of Childhood and Adolescent Obesity with Stress Through the Analysis of Multidimensional Data with Machine Learning and Artificial Intelligence Tools.Advances in experimental medicine and biology · 2026Article
- Demographics and obesity using a machine learning approach: Iranian National Obesity Registry (IRNOR).Journal of education and health promotion · 2026Article
- Transforming Obesity Care With Digital Therapeutics: Mobile Health Apps, Clinical Evidence, and Regulatory Considerations.Journal of obesity · 2026Review
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- Uncovering age-specific subtypes of pediatric obesity and metabolic syndrome using machine learning algorithms.Scientific reports · 2025Article
- Evaluating algorithmic fairness of machine learning models in predicting underweight, overweight, and adiposity across socioeconomic and caste groups in India: evidence from the longitudinal ageing study in India.PLOS digital health · 2025Article
- Visual analysis of research hot topics and trends of clinical decision support system based on CiteSpace.Langenbeck's archives of surgery · 2025Review
- Risk factors and prediction for pediatric obesity: current status and future perspectives.Endocrine journal · 2025Review
- Development of Machine Learning-Based Risk Prediction Models to Predict Rapid Weight Gain in Infants: Analysis of Seven Cohorts.JMIR public health and surveillance · 2025Article
- Evaluating clinicians' attitudes toward a web-based tool to support culturally and medically tailored nutrition services at the point of care.JAMIA open · 2025Article
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Prediction of obesity levels based on physical activity and eating habits with a machine learning model integrated with explainable artificial intelligence.Frontiers in physiology · 2025Article
- Reliable prediction of childhood obesity using only routinely collected EHRs may be possible.Obesity pillars · 2024Article
- Validation of Variables for Use in Pediatric Obesity Risk Score Development in Demographically and Racially Diverse United States Cohorts.The Journal of pediatrics · 2024Article
- Multiple omics reveal link between the microbiota-gut-brain axis and intracranial aneurysm rupture.iScience · 2024Article
8 more citing papers are in PubMed but not listed here.
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Authors and funding
4 authors.
Funding
Abstract
objectivesThis paper aims to predict childhood obesity after age two, using only data collected prior to the second birthday by a clinical decision support system called CHICA.
methodsAnalyses of six different machine learning methods: RandomTree, RandomForest, J48, ID3, Naïve Bayes, and Bayes trained on CHICA data show that an accurate, sensitive model can be created.
resultsOf the methods analyzed, the ID3 model trained on the CHICA dataset proved the best overall performance with accuracy of 85% and sensitivity of 89%. Additionally, the ID3 model had a positive predictive value of 84% and a negative predictive value of 88%. The structure of the tree also gives insight into the strongest predictors of future obesity in children. Many of the strongest predictors seen in the ID3 modeling of the CHICA dataset have been independently validated in the literature as correlated with obesity, thereby supporting the validity of the model.
conclusionsThis study demonstrated that data from a production clinical decision support system can be used to build an accurate machine learning model to predict obesity in children after age two.
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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.