AIMC Topic: Obesity
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Obesity classification: a comparative study of machine learning models excluding weight and height data.
Revista da Associacao Medica Brasileira (1992)
Mar 17, 2025
OBJECTIVE: Obesity is a global health problem. The aim is to analyze the effectiveness of machine learning models in predicting obesity classes and to determine which model performs best in obesity classification.
Identifying individuals at risk for weight gain using machine learning in electronic medical records from the United States.
Diabetes, obesity & metabolism
Mar 11, 2025
AIMS: Numerous risk factors for the development of obesity have been identified, yet the aetiology is not well understood. Traditional statistical methods for analysing observational data are limited by the volume and characteristics of large dataset...
Development and multi-center cross-setting validation of an explainable prediction model for sarcopenic obesity: a machine learning approach based on readily available clinical features.
Aging clinical and experimental research
Mar 1, 2025
OBJECTIVES: Sarcopenic obesity (SO), characterized by the coexistence of obesity and sarcopenia, is an increasingly prevalent condition in aging populations, associated with numerous adverse health outcomes. We aimed to identify and validate an expla...
Machine learning approach on plasma proteomics identifies signatures associated with obesity in the KORA FF4 cohort.
Diabetes, obesity & metabolism
Feb 27, 2025
AIMS: This study investigated the role of plasma proteins in obesity to identify predictive biomarkers and explore underlying biological mechanisms.
The Role of Artificial Intelligence in Obesity Risk Prediction and Management: Approaches, Insights, and Recommendations.
Medicina (Kaunas, Lithuania)
Feb 19, 2025
Greater than 650 million individuals worldwide are categorized as obese, which is associated with significant health, economic, and social challenges. Given its overlap with leading comorbidities such as heart disease, innovative solutions are necess...
Leveraging OGTT derived metabolic features to detect Binge-eating disorder in individuals with high weight: a "seek out" machine learning approach.
Translational psychiatry
Feb 18, 2025
Binge eating disorder (BED) carries a 6 times higher risk for obesity and accounts for roughly 30% of type 2 diabetes cases. Timely identification of early glycemic disturbances and comprehensive treatment can impact on the likelihood of associated m...
Development and clinical evaluation of an AI-assisted respiratory state classification system for chest X-rays: A BMI-Specific approach.
Computers in biology and medicine
Feb 16, 2025
PURPOSE: In this study, we aimed to develop and clinically evaluate an artificial intelligence (AI)-assisted support system for determining inhalation and exhalation states on chest X-ray images, focusing on the specific challenge of respiratory stat...
Functionally characterizing obesity-susceptibility genes using CRISPR/Cas9, in vivo imaging and deep learning.
Scientific reports
Feb 13, 2025
Hundreds of loci have been robustly associated with obesity-related traits, but functional characterization of candidate genes remains a bottleneck. Aiming to systematically characterize candidate genes for a role in accumulation of lipids in adipocy...
Transfer Learning Prediction of Early Exposures and Genetic Risk Score on Adult Obesity in Two Minority Cohorts.
Prevention science : the official journal of the Society for Prevention Research
Feb 6, 2025
Due to ethnic heterogeneity in genetic architecture, genetic risk score (GRS) constructed within the European population generally possesses poor portability in underrepresented non-European populations, but substantial genetic similarity exists acro...
Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.
JMIR medical informatics
Feb 5, 2025
BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be e...
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