Synapse
⌘+K
Synapse
PulseExploreClubsResearchersJournals
Instagram
HomeClubsExplore
August 22, 2025Frontiers in Public HealthOpen Access

A machine learning model for predicting obesity risk in patients with diabetes mellitus: analysis of NHANES 2007–2018

View Full Paper
Ask AI
Bookmark
Share

Authors

WWWenqiang WangRMRuiqing MoXCXingyu Chen

Discussion

Loading...

Member takes

Overview

Machine learning model predicts obesity risk in diabetes patients, suggesting key features for effective management.

Key Points

  • Logistic regression model achieved an AUC of 0.781 in predicting obesity risk among patients with diabetes.
  • A total of 3,794 participants with diabetes were analyzed, with 57% identified as obese, highlighting the prevalence of obesity.
  • LASSO regression identified 19 significant variables linked to obesity risk, enhancing predictive outcomes.
  • A nomogram based on the logistic regression model aids in individual risk prediction and management of obesity.

Cite This Study

Wang et al. (2025) studied this question.

synapsesocial.com/papers/68af73697567bf4f94fed700https://doi.org/10.3389/fpubh.2025.1606751
View Full Paper
Ask AI
Bookmark
Share

Also Consider

Synapse has enriched 5 closely related papers on similar clinical questions. Consider them for comparative context:

  1. 1Machine learning predicts diabetes risk in high-risk populations: based on the National Health and Nutrition Examination Survey database2025 · 2 citations
  2. 2AI-driven analysis of diabetes risk determinants in U.S. adults: Exploring disease prevalence and health factors2025 · 1 citations
  3. 3Development and internal validation of a machine learning algorithm for the risk of type 2 diabetes mellitus in children with obesity2025 · 2 citations
  4. 4Machine Learning Models for Diabetes Prediction: Logistic Regression, SVM, Random Forest, and Neural Networks2025
  5. 5Diabetes Risk Prediction Model Using Machine Learning2025 · 2 citations