ArticleScientific reports2024
Visualization obesity risk prediction system based on machine learning.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 24 papers, 1 of them a synthesis that pooled it.
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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
24 citing papers in PubMed, 1 synthesis or guideline pooled it.
- iCARDIO Alliance Global Implementation Guidelines for the Management of Obesity 2025.Journal of cachexia, sarcopenia and muscle · 2026Guideline
- iCARDIO Alliance global implementation guidelines for the management of obesity 2025 focus on prevention and treatment of cardiometabolic disease.American journal of preventive cardiology · 2026Article
- Multimodal (Bio)Markers and Risk of Obesity - A Comprehensive Scoping Review.Advances in nutrition (Bethesda, Md.) · 2026Article
- A study on promoting AI learning and usage behaviors among health management students from the perspective of the "knowledge-belief-action" model.Frontiers in public health · 2026Article
- "Short" is not always "scientific": cross-sectional quality assessment and machine learning-based evaluation of weight management short videos on TikTok and Bilibili.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
- Development and validation of a machine learning-based risk prediction model for sarcopenia in community hospital patients: a retrospective cohort study.Frontiers in aging · 2026Article
- Integrated Assessment of Obesity Indices and Novel Inflammatory Biomarkers in Predicting the Severity of Obstructive Sleep Apnea.Journal of clinical medicine · 2025Article
- Obesity prediction using an explainable deep learning framework based on LSTM-LIME with integrated visualization.Scientific reports · 2025Article
- Artificial Intelligence in Obesity Prevention.Healthcare (Basel, Switzerland) · 2025Review
- AI in Adipose Imaging: Revolutionizing Visceral Adipose Tissue, Ectopic Fat, and Cardiovascular Risk Assessment.Current atherosclerosis reports · 2025Review
- Machine learning framework for predicting susceptibility to obesity.Scientific reports · 2025Article
- Neighborhood Environmental and Contextual Factors Improve Prediction of Childhood Body Mass Index: An Application of Novel Graph Neural Networks.AJE advances : research in epidemiology · 2025Article
- The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.Medicina (Kaunas, Lithuania) · 2025Review
- AI Machine Learning-Based Diabetes Prediction in Older Adults in South Korea: Cross-Sectional Analysis.JMIR formative research · 2025Article
- Association Between BMI and Neurocognitive Functions Among Middle-aged Obese Adults: Preliminary Findings Using Machine-learning (ML)-based Approach.Annals of neurosciences · 2025Article
- 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
- Predicting the risk of metabolic-associated fatty liver disease in the elderly population in China: construction and evaluation of interpretable machine learning models.Frontiers in medicine · 2025Article
- Spatiotemporal prediction of obesity rates and model interpretability analysis from a public health perspective.PloS one · 2025Article
- Construction and validation of nomogram prediction model for anxiety and depression in chemotherapy patients with multiple myeloma.Frontiers in psychiatry · 2025Article
Corrections and comments
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Authors and funding
6 authors.
Funding
Abstract
Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and progression are required. In this study, we developed a visualized obesity risk prediction system based on machine learning techniques, aiming to achieve personalized comprehensive health management for obesity. The system utilized a dataset consisting of 1678 anonymized health examination records, including individual lifestyle factors, body composition, blood routine, and biochemical tests. Ten multi-classification machine learning models, including Random Forest and XGBoost, were constructed to identify non-obese individuals (BMI < 25), class 1 obese individuals (25 ≤ BMI < 30), and class 2 obese individuals (30 ≤ BMI). By evaluating the performance of each model on the test set, we selected XGBoost as the best model and built the visualized obesity risk prediction system based on it. The system exhibited good predictive performance and interpretability, directly providing users with their obesity risk levels and determining corresponding intervention priorities. In conclusion, the developed obesity risk prediction system possesses high accuracy and interactivity, aiding physicians in formulating personalized health management plans and achieving comprehensive and accurate obesity management.
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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 Socratic graph.