Diet & Nutrition

A machine learning model predicts stroke associated with blood cadmium level.

Stroke is the leading cause of death and disability worldwide. Cadmium is a prevalent environmental toxicant that may contribute to cardiovascular disease, including stroke. We aimed to build an effective and interpretable machine learning (ML) model that links blood cadmium to the identification of stroke. Our data exploring the association between blood cadmium and stroke came from the National ...

AI nutrition recommendation using a deep generative model and ChatGPT.

In recent years, major advances in artificial intelligence (AI) have led to the development of powerful AI systems for use in the field of nutrition in order to enhance personalized dietary recommendations and improve overall health and well-being. However, the lack of guidelines from nutritional experts has raised questions on the accuracy and trustworthiness of the nutritional advice provided by...

Neural network model for prediction of possible sarcopenic obesity using Korean national fitness award data (2010-2023).

Sarcopenic obesity (SO) is characterized by concomitant sarcopenia and obesity and presents a high risk of disability, morbidity, and mortality among ...

A machine learning (ML) approach to understanding participation in government nutrition programs.

Machine Learning (ML) affords researchers tools to advance beyond research methods commonly employed in psychology, business, and public policy studie...

Intellectual assessment of amyotrophic lateral sclerosis using deep resemble forward neural network.

ALS (Amyotrophic Lateral Sclerosis) is a neurodegenerative disorder causing profound physical disability that severely impairs a patient's life expect...

Deep learning for osteoporosis screening using an anteroposterior hip radiograph image.

PURPOSE: Osteoporosis is a common bone disorder characterized by decreased bone mineral density (BMD) and increased bone fragility, which can lead to ...

A Data-Driven Approach to Predicting Recreational Activity Participation Using Machine Learning.

With the popularity of recreational activities, the study aimed to develop prediction models for recreational activity participation and explore the ...

ChatGPT: A Conceptual Review of Applications and Utility in the Field of Medicine.

Artificial Intelligence, specifically advanced language models such as ChatGPT, have the potential to revolutionize various aspects of healthcare, med...

AI nutritionist: Intelligent software as the next generation pioneer of precision nutrition.

With the rapid development of information technology and artificial intelligence (AI), people have acquired the abilities and are encouraged to develo...

Pleiotropic Effects of Direct Oral Anticoagulants in Chronic Heart Failure and Atrial Fibrillation: Machine Learning Analysis.

Oral anticoagulant therapy (OAT) for managing atrial fibrillation (AF) encompasses vitamin K antagonists (VKAs, such as warfarin), which was the mains...

Relationships between minerals' intake and blood homocysteine levels based on three machine learning methods: a large cross-sectional study.

BACKGROUND: Blood homocysteine (Hcy) level has become a sensitive indicator in predicting the development of cardiovascular disease. Studies have show...

Using explainable machine learning and fitbit data to investigate predictors of adolescent obesity.

Sociodemographic and lifestyle factors (sleep, physical activity, and sedentary behavior) may predict obesity risk in early adolescence; a critical pe...

Application of a transparent artificial intelligence algorithm for US adults in the obese category of weight.

OBJECTIVE AND AIMS: Identification of associations between the obese category of weight in the general US population will continue to advance our unde...

Detection of milk adulteration using coffee ring effect and convolutional neural network.

A low-cost and effective method is reported to identify water and synthetic milk adulteration of cow's milk using coffee ring patterns. The cow's milk...

A Quadruple Revolution: Deciphering Biological Complexity with Artificial Intelligence, Multiomics, Precision Medicine, and Planetary Health.

A quiet quadruple revolution has been in the making in systems science with convergence of (1) artificial intelligence, machine learning, and other di...

Does machine learning have a high performance to predict obesity among adults and older adults? A systematic review and meta-analysis.

AIM: Machine learning may be a tool with the potential for obesity prediction. This study aims to review the literature on the performance of machine ...

Machine Learning Identification of Nutrient Intake Variations across Age Groups in Metabolic Syndrome and Healthy Populations.

This study undertakes a comprehensive examination of the intricate link between diet nutrition, age, and metabolic syndrome (MetS), utilizing advanced...

Differentiating Microplastics from Natural Particles in Aqueous Suspensions Using Flow Cytometry with Machine Learning.

Microplastics (MPs) in natural waters are heterogeneously mixed with other natural particles including algal cells and suspended sediments. An easy-to...

Machine-learning models for diagnosis of rotator cuff tears in osteoporosis patients based on anteroposterior X-rays of the shoulder joint.

OBJECTIVE: This study aims to diagnose Rotator Cuff Tears (RCT) and classify the severity of RCT in patients with Osteoporosis (OP) through the analys...

The development of an EU-wide nutrition and physical activity expert knowledge base to support a personalised mobile application across various EU population groups.

A healthy lifestyle comprising regular physical activity and an adequate diet is imperative for the prevention of non-communicable diseases such as hy...