AIMC Topic: Nutrition Surveys
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Machine learning-based disease risk stratification and prediction of metabolic dysfunction-associated fatty liver disease using vibration-controlled transient elastography: Result from NHANES 2021-2023.
BMC gastroenterology
Apr 14, 2025
BACKGROUND: Metabolic dysfunction-associated fatty liver disease (MAFLD) is a common chronic liver disease and represents a significant public health issue. Nevertheless, current risk stratification methods remain inadequate. The study aimed to use m...
Predicting metabolic dysfunction associated steatotic liver disease using explainable machine learning methods.
Scientific reports
Apr 11, 2025
Early and accurate identification of patients at high risk of metabolic dysfunction-associated steatotic liver disease (MASLD) is critical to prevent and improve prognosis potentially. We aimed to develop and validate an explainable prediction model ...
The Association of Elevated Depression Levels and Life's Essential 8 on Cardiovascular Health With Predicted Machine Learning Models and Interpretations: Evidence From NHANES 2007-2018.
Depression and anxiety
Apr 10, 2025
The association between depression severity and cardiovascular health (CVH) represented by Life's Essential 8 (LE8) was analyzed, with a novel focus on ranked levels and different ages. Machine learning (ML) algorithms were also selected aimed at pr...
Machine learning integration of multimodal data identifies key features of circulating NT-proBNP in people without cardiovascular diseases.
Scientific reports
Apr 8, 2025
N-Terminal Pro-Brain Natriuretic Peptide (NT-proBNP) is important for diagnosing and predicting heart failure or many other diseases. However, few studies have comprehensively assessed the factors correlated with NT-proBNP levels in people with cardi...
Machine learning based association between inflammation indicators (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality in arthritis patients with hypertension: NHANES 1999-2018.
Frontiers in public health
Apr 4, 2025
BACKGROUND: This study aimed to evaluate the relationship between CBC-derived inflammatory markers (NLR, PLR, NPAR, SII, SIRI, and AISI) and all-cause mortality (ACM) risk in arthritis (AR) patients with hypertensive (HTN) using data from the NHANES.
Development and validation of a machine learning model for predicting pediatric metabolic syndrome using anthropometric and bioelectrical impedance parameters.
International journal of obesity (2005)
Apr 1, 2025
OBJECTIVE: Metabolic syndrome (MS) is a risk factor for cardiovascular diseases, and its prevalence is increasing among children and adolescents. This study developed a machine learning model to predict MS using anthropometric and bioelectrical imped...
The interpretable machine learning model for depression associated with heavy metals via EMR mining method.
Scientific reports
Mar 28, 2025
Limited research exists on the association between depression and heavy metal exposure. This study aims to develop an interpretable and efficient machine learning (ML) model with robust performance to identify depression linked to heavy metal exposur...
Learning from the machine: is diabetes in adults predicted by lifestyle variables? A retrospective predictive modelling study of NHANES 2007-2018.
BMJ open
Mar 22, 2025
OBJECTIVES: This study aimed to compare the performance of five machine learning algorithms to predict diabetes mellitus based on lifestyle factors (diet and physical activity).
Network-based predictive models for artificial intelligence: an interpretable application of machine learning techniques in the assessment of depression in stroke patients.
BMC geriatrics
Mar 22, 2025
BACKGROUND: Depression is a common complication after a stroke that may lead to increased disability and decreased quality of life. The objective of this study was to develop and validate an interpretable predictive model to assess the risk of depres...
Machine learning analysis of cardiovascular risk factors and their associations with hearing loss.
Scientific reports
Mar 22, 2025
Hearing loss poses immense burden worldwide and early detection is crucial. The accurate models identify high-risk groups, enabling timely intervention to improve quality of life. The subtle changes in hearing often go unnoticed, presenting a challen...
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