Diet & Nutrition

Identification of metabolism related biomarkers in obesity based on adipose bioinformatics and machine learning.

BACKGROUND: Obesity has emerged as a growing global public health concern over recent decades. Obesity prevalence exhibits substantial global variation, ranging from less than 5% in regions like China, Japan, and Africa to rates exceeding 75% in urban areas of Samoa.

Trends in the prevalence of osteoporosis and effects of heavy metal exposure using interpretable machine learning.

There is limited evidence that heavy metals exposure contributes to osteoporosis. Multi-parameter scoring machine learning (ML) techniques were developed using National Health and Nutrition Examination Survey data to predict osteoporosis based on heavy metal exposure levels. For generating an optimal predictive model for osteoporosis, 12 ML models were used. Identification was carried out using th...

Impact of nutrition-related laboratory tests on mortality of patients who are critically ill using artificial intelligence: A focus on trace elements, vitamins, and cholesterol.

BACKGROUND: This study aimed to understand the collective impact of trace elements, vitamins, cholesterol, and prealbumin on patient outcomes in the i...

Machine Learning Models for Predicting Significant Liver Fibrosis in Patients with Severe Obesity and Nonalcoholic Fatty Liver Disease.

PURPOSE: Although noninvasive tests can be used to predict liver fibrosis, their accuracy is limited for patients with severe obesity and nonalcoholic...

An explainable machine learning estimated biological age based on morphological parameters of the spine.

Accurately estimating biological age is beneficial for measuring aging and predicting risk. It is widely accepted that the prevalence of spine compres...

Sustainable separation of molybdenum from mixed mineral acids generated as semiconductor industry waste streams using tributyl phosphate (TBP) by effects of hybrid machine learning models.

This study explores the separation and optimization of molybdenum (Mo) from mixed mineral acids derived from semiconductor industry waste streams with...

Modeling health risks using neural network ensembles.

This study aims to demonstrate that demographics combined with biometrics can be used to predict obesity related chronic disease risk and produce a he...

Enhancing dietary analysis: Using machine learning for food caloric and health risk assessment.

In the wake of growing concerns regarding diet-related health issues, this study investigates the application of machine learning methods to estimate ...

The Bioprotective Effects of Marigold Tea Polyphenols on Obesity and Oxidative Stress Biomarkers in High-Fat-Sugar Diet-Fed Rats.

The research is aimed at exploring the potential of marigold petal tea (MPT), rich in polyphenol contents, against oxidative stress and obesity in a ...

Valid inference for machine learning-assisted genome-wide association studies.

Machine learning (ML) has become increasingly popular in almost all scientific disciplines, including human genetics. Owing to challenges related to s...

Visualization obesity risk prediction system based on machine learning.

Obesity is closely associated with various chronic diseases.Therefore, accurate, reliable and cost-effective methods for preventing its occurrence and...

Controlled and Real-Life Investigation of Optical Tracking Sensors in Smart Glasses for Monitoring Eating Behavior Using Deep Learning: Cross-Sectional Study.

BACKGROUND: The increasing prevalence of obesity necessitates innovative approaches to better understand this health crisis, particularly given its st...

Applying machine learning approaches for predicting obesity risk using US health administrative claims database.

INTRODUCTION: Body mass index (BMI) is inadequately recorded in US administrative claims databases. We aimed to validate the sensitivity and positive ...

Maternal dietary practices during pregnancy and obesity of neonates: a machine learning approach towards hierarchical and nested relationships in a Tibet Plateau cohort study.

Studies on obesity and risk factors from a life-course perspective among residents in the Tibet Plateau with recent economic growth and increasing obe...

Predicting leaf nitrogen content in wolfberry trees by hyperspectral transformation and machine learning for precision agriculture.

Leaf nitrogen content (LNC) is an important indicator for scientific diagnosis of the nutrition status of crops. It plays an important role in the gro...

Deep learning enabled in vitro predicting biological tissue thickness using force measurement device.

Accurate perception of biological tissues (BT) thickness is essential for preliminary evaluation of medical diagnosis and animal nutrition. However, t...

Estimating Breakfast Characteristics Using Continuous Glucose Monitoring and Machine Learning in Adults With or at Risk of Type 2 Diabetes.

BACKGROUND: Continuous glucose monitoring (CGM) systems allow detailed assessment of postprandial glucose responses (PPGR), offering new insights into...

Predicting malnutrition-based anemia in geriatric patients using machine learning methods.

BACKGROUND: Anemia due to malnutrition may develop as a result of iron, folate and vitamin B12 deficiencies. This situation poses a higher risk of mor...

Neuro-fuzzy prediction model of occupational injuries in mining.

This study investigates the possibility of developing a unique model for predicting work-related injuries in Serbian underground coal mines using neu...

Application of machine vision in food computing: A review.

With global intelligence advancing and the awareness of sustainable development growing, artificial intelligence technology is increasingly being appl...