Obesity

Prediction of p-phenylenediamine antioxidant concentrations in human urine using machine learning models.

p-phenylenediamine antioxidants (PPDs) are extensively used in rubber manufacturing for their potent antioxidative properties, but PPDs and 2-anilino-5-[(4-methylpentan-2yl)amino]cyclohexa-2,5-diene-1,4-dione (6PPDQ) pose potential environmental and health risks. Existing biomonitoring methods for assessing human exposure to PPDs are labor-intensive, costly, and provide limited data. Thus, there i...

Predictive models and determinants of mortality among T2DM patients in a tertiary hospital in Ghana, how do machine learning techniques perform?

BACKGROUND: The increasing prevalence of type 2 diabetes mellitus (T2DM) in lower and middle - income countries call for preventive public health interventions. Studies from Africa including those from Ghana, consistently reveal high T2DM-related mortality rates. While previous research in the Ho municipality has primarily examined risk factors, comorbidity, and quality of life of T2DM patients, t...

Partial directed coherence analysis of resting-state EEG signals for alcohol use disorder detection using machine learning.

INTRODUCTION: Excessive alcohol consumption negatively impacts physical and psychiatric health, lifestyle, and societal interactions. Chronic alcohol ...

Exploring the shared gene signatures and mechanism among three autoimmune diseases by bulk RNA sequencing integrated with single-cell RNA sequencing analysis.

BACKGROUND: Emerging evidence underscores the comorbidity mechanisms among autoimmune diseases (AIDs), with innovative technologies such as single-cel...

Multimodal machine learning for analysing multifactorial causes of disease-The case of childhood overweight and obesity in Mexico.

BACKGROUND: Mexico has one of the highest global incidences of paediatric overweight and obesity. Public health interventions have shown only moderate...

Investigation and Assessment of AI's Role in Nutrition-An Updated Narrative Review of the Evidence.

BACKGROUND: Artificial Intelligence (AI) technologies are now essential as the agenda of nutrition research expands its scope to look at the intricate...

Development and validation of a new nomogram for self-reported OA based on machine learning: a cross-sectional study.

Developing a new diagnostic prediction model for osteoarthritis (OA) to assess the likelihood of individuals developing OA is crucial for the timely i...

Interpretable machine learning for identifying overweight and obesity risk factors of older adults in China.

OBJECTIVE: To estimate the importance of risk factors on overweight/obesity among older adults by comparing different predictive model.

A novel fuzzy three-valued logic computational framework in machine learning for medicine dataset.

For consideration of uncertainties of a medicine dataset, a new conceptual architecture fuzzy three-valued logic is introduced in this research work. ...

Employing a low-code machine learning approach to predict in-hospital mortality and length of stay in patients with community-acquired pneumonia.

Community-acquired pneumonia (CAP) is associated with high mortality rates and often results in prolonged hospital stays. The potential of machine lea...

Exercise improves body composition, physical fitness, and blood levels of C-peptide and IGF-1 in 11- to 12-year-old boys with obesity.

INTRODUCTION: Exercise is vital in preventing and treating obesity. Despite its importance, the understanding of how exercise influences childhood obe...

Analyzing Secondary Cancer Risk: A Machine Learning Approach.

OBJECTIVE: Addressing the rising cancer rates through timely diagnosis and treatment is crucial. Additionally, cancer survivors need to understand the...

Using Machine Learning to Predict Weight Gain in Adults: an Observational Analysis From the All of Us Research Program.

INTRODUCTION: Obesity, defined as a body mass index ≥30 kg/m, is a major public health concern in the United States. Preventative approaches are essen...

Stratifying vascular disease patients into homogeneous subgroups using machine learning and FLAIR MRI biomarkers.

This study proposes a framework to stratify vascular disease patients based on brain health and cerebrovascular disease (CVD) risk using regional FLAI...

Web application using machine learning to predict cardiovascular disease and hypertension in mine workers.

This study presents a web application for predicting cardiovascular disease (CVD) and hypertension (HTN) among mine workers using machine learning (ML...

Utilising causal inference methods to estimate effects and strategise interventions in observational health data.

Randomised controlled trials (RCTs) are the gold standard for evaluating health interventions but often face ethical and practical challenges. When RC...

Estimating cardiovascular mortality in patients with hypertension using machine learning: The role of depression classification based on lifestyle and physical activity.

PURPOSE: This study aims to harness machine learning techniques, particularly the Random Survival Forest (RSF) model, to assess the impact of depressi...

Clustering and classification for dry bean feature imbalanced data.

The traditional machine learning methods such as decision tree (DT), random forest (RF), and support vector machine (SVM) have low classification perf...

Predicting host health status through an integrated machine learning framework: insights from healthy gut microbiome aging trajectory.

The gut microbiome, recognized as a critical component in the development of chronic diseases and aging processes, constitutes a promising approach fo...

Machine learning prediction model of the treatment response in schizophrenia reveals the importance of metabolic and subjective characteristics.

Predicting early treatment response in schizophrenia is pivotal for selecting the best therapeutic approach. Utilizing machine learning (ML) technique...