ReviewObesity reviews : an official journal of the International Association for the Study of Obesity2018
A review of machine learning in obesity.
Review in Obesity reviews : an official journal of the International Association for the Study of Obesity, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 96 papers, 5 of them syntheses that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
96 citing papers in PubMed, 5 syntheses or guidelines pooled it.
- Machine Learning Prediction Models for Weight Loss Outcomes after Metabolic and Bariatric Surgery: a Systematic Review and Meta-Analysis.Obesity surgery · 2026Pooled it
- A Systematic Review on Applications of Artificial Intelligence for Obesity Prevention.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Pooled it
- Meteorological factors cannot be ignored in machine learning-based methods for predicting dengue, a systematic review.International journal of biometeorology · 2024Pooled it
- Prediction model for cognitive frailty in older adults: A systematic review and critical appraisal.Frontiers in aging neuroscience · 2023Pooled it
- Modeling obesity in complex food systems: Systematic review.Frontiers in endocrinology · 2022Pooled it
- Artificial Intelligence Interventions Targeting Obesity-Related Behaviors: Protocol for a Scoping Review.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2026Article
- Machine Learning-Based Prediction of Brain Metastasis at Initial Diagnosis in Small-Cell Lung Cancer: Model Development and SHAP Interpretation Study.Cancer reports (Hoboken, N.J.) · 2026Article
- 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
- Engineering Nanomaterials for Raman Bioimaging and Theranostics: Advances, Challenges, and Future Directions.ACS omega · 2025Review
- Lifestyle data-based multiclass obesity prediction with interpretable ensemble models incorporating SHAP and LIME analysis.Scientific reports · 2025Article
- Article
- Advancements in the diagnosis of biliopancreatic diseases: A comparative review and study on future insights.World journal of gastrointestinal endoscopy · 2025Review
- Harnessing Artificial Intelligence in Obesity Research and Management: A Comprehensive Review.Diagnostics (Basel, Switzerland) · 2025Review
- Machine learning empowered coherent Raman imaging and analysis for biomedical applications.Communications engineering · 2025Review
- Environmental factors affecting the BMI of older adults in the Philippines spatially assessed using machine learning.Heliyon · 2025Article
- SHAP-enhanced machine learning identifies modifiable obesity predictors across adolescent weight groups: A 2021 YRBSS analysis.PloS one · 2025Article
- Computational algorithm based on health and lifestyle traits to categorize lifemetabotypes in the NUTRiMDEA cohort.Scientific reports · 2024Article
- Prediction of preterm birth in multiparous women using logistic regression and machine learning approaches.Scientific reports · 2024Article
- Identifying factors related to mortality of hospitalized COVID-19 patients using machine learning methods.Heliyon · 2024Article
- Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.European journal of nutrition · 2024Article
36 more citing papers are in PubMed but not listed here.
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
12 authors.
Funding
Abstract
Rich sources of obesity-related data arising from sensors, smartphone apps, electronic medical health records and insurance data can bring new insights for understanding, preventing and treating obesity. For such large datasets, machine learning provides sophisticated and elegant tools to describe, classify and predict obesity-related risks and outcomes. Here, we review machine learning methods that predict and/or classify such as linear and logistic regression, artificial neural networks, deep learning and decision tree analysis. We also review methods that describe and characterize data such as cluster analysis, principal component analysis, network science and topological data analysis. We introduce each method with a high-level overview followed by examples of successful applications. The algorithms were then applied to National Health and Nutrition Examination Survey to demonstrate methodology, utility and outcomes. The strengths and limitations of each method were also evaluated. This summary of machine learning algorithms provides a unique overview of the state of data analysis applied specifically to obesity.
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What OpenQuestion holds
Registered trials
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.