Obesity

A hybrid feature selection framework combining Artificial Bee Colony and decision trees for CVD risk assessment.

Cardiovascular Disease (CVD) risk assessment involves evaluating various clinical and lifestyle factors to estimate an individual's likelihood of developing heart-related conditions. Accurate risk prediction helps in early intervention and preventive care. Min-max scaling is applied during pre-processing to ensure all input features are normalized to a standard scale, typically within a given rang...

Biomarker-anchored screening of dietary microplastics via a gut microbiota-informed machine learning model.

Microplastics (MPs) are increasingly recognized as emerging contaminants in the human diet, yet the absence of unified biomarker-anchored screening thresholds hampers quantitative risk assessment. This study establishes a gut microbiota-anchored machine learning framework that links Firmicutes/Bacteroidetes (F/B) ratio thresholds with real-world dietary MP exposure data, enabling a biomarker-ancho...

The Dark Side of Love: Prediction of Digital Intimate Partner Violence and Associated Factors Among University Students Using Machine Learning.

This study aimed to identify key risk factors and predict digital intimate partner violence (DIPV) exposure and perpetration among university students...

Serum metabolic profile at clinical onset as predictor of multiple sclerosis activity and progression after 5 years.

BACKGROUND: Metabolic dysregulations have been reported in multiple sclerosis (MS), but it is unclear whether metabolic profiles at disease onset pred...

Optimizing temporal windows for wearable-augmented post-discharge risk prediction: a methods study.

OBJECTIVE: Traditional readmission risk models relying on static discharge data have limited predictive performance and fail to capture patients' reco...

An explainable clinical-radiomics machine learning model for preoperative prediction of WHO/ISUP nuclear grade in clear cell renal cell carcinoma.

BACKGROUND: Accurate preoperative assessment of the WHO/ISUP nuclear grade of clear cell renal cell carcinoma (ccRCC) is critical for guiding individu...

Coupled cross-sectional and longitudinal non-negative matrix factorization reveals dominant brain aging trajectories in 48,949 individuals.

Machine learning can unravel heterogeneous patterns of brain aging and neurodegeneration, but existing methods offer limited insights into disease pro...

The ecological framework of population health - Adding public trust as a forcing factor for U.S. life expectancy and COVID-19 mortality.

OBJECTIVES: What are the factors that set the stage for health status and outcomes in the United States (U.S.)? This complex question is rarely consid...

Machine Learning-Based Magnetocardiography Model Aids in Diagnosing Non-ST-Segment Elevation Acute Coronary Syndrome in Acute Chest Pain.

BACKGROUND: Non-ST-segment elevation acute coronary syndrome (NSTE-ACS) is a leading cause of acute chest pain in clinical practice. Magnetocardiograp...

Exploring schizophrenia-associated image-derived digital phenotypes in dual-energy X-ray densitometer scans of non-dominant distal forearm: An explainable machine-learning framework.

Schizophrenia remains diagnosed primarily through clinical assessment, which motivates the researchers to search for quantifiable digital phenotypes. ...

Association of the neutrophil percentage-to-albumin ratio with sarcopenia in U.S. adults: Evidence from NHANES 2011-2018 and machine learning-based analyses.

BACKGROUND: Sarcopenia is an age-related muscle disorder driven by complex interactions between chronic inflammation and nutritional imbalance. The ne...

Fetal Fraction and Uterine Leiomyoma Volume: New Insights From Interpretable Modeling.

OBJECTIVE: This study aims to develop a predictive model to estimate the likelihood of achieving a sufficient fetal fraction (FF) for non-invasive pre...

Unsupervised Agglomerative Cluster Phenotyping of Young Patients With Acute Coronary Syndrome Undergoing Percutaneous Coronary Intervention.

BACKGROUND: Young patients with acute coronary syndrome (ACS) exhibit diverse demographic, clinical and angiographic characteristics. We hypothesized ...

Precision nutrition in breast cancer: Towards patient- and tumour-informed dietary strategies.

Dietary strategies are increasingly recognized as important modulators of breast cancer outcomes, acting through effects on metabolic regulation, weig...

Comparative evaluation of obesity-related indices for predicting incident hypertension: evidence from Chinese and UK longitudinal cohorts with machine learning interpretation.

From Bio-Interface Materials to Neural Integration: The Next-Generation Brain-Machine Interfaces Powered by Hydrogels.

Brain-machine interfaces (BMIs), which serve as revolutionary tools for neural recording, modulation, and rehabilitation, are highly dependent on the ...

Amyloidosis secondary to familial Mediterranean fever: machine learning-based prediction models.

OBJECTIVES: Familial Mediterranean Fever (FMF) is a monogenic autoinflammatory disease caused by MEFV mutations, with amyloidosis as its most severe c...

Gut microbiome and metabolic health: mechanisms and precision interventions.

The gut microbiome is increasingly recognized as a fundamental regulator of metabolic health, shaping energy balance, insulin sensitivity, inflammator...

Assessing the Impact of Extreme Climate Events on Cardiovascular Disease in 157 Chinese Cities (2015-2020): A Spatial and Causal Analysis.

INTRODUCTION: Extreme climate events worsen cardiovascular disease burden, exacerbated by rapid aging. Prior studies have linked extreme climate event...

The Effect of Age, Sex, and Body Mass Index on Spinopelvic Balance Throughout Childhood and Adolescence: An Automated 3D-Computed Tomography Study of 9,721 Pediatric Patients.

BACKGROUND: Spinopelvic assessment is critical for surgical planning. It is unknown if age, sex, and body mass index (BMI) influence pelvic orientatio...