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Identification of Clusters in a Population With Obesity Using Machine Learning: Secondary Analysis of The Maastricht Study.

BACKGROUND: Modern lifestyle risk factors, like physical inactivity and poor nutrition, contribute to rising rates of obesity and chronic diseases like type 2 diabetes and heart disease. Particularly personalized interventions have been shown to be effective for long-term behavior change. Machine learning can be used to uncover insights without predefined hypotheses, revealing complex relationship...

Comparative analysis of the DCNN and HFCNN Based Computerized detection of liver cancer.

Liver cancer detection is critically important in the discipline of biomedical image testing and diagnosis. Researchers have explored numerous machine learning (ML) techniques and deep learning (DL) approaches aimed at the automated recognition of liver disease by analysing computed tomography (CT) images. This study compares two frameworks, Deep Convolutional Neural Network (DCNN) and Hierarchica...

Semi-supervised learning-based identification of the attachment between sludge and microparticles in wastewater treatment.

Monitoring the microparticle transfer process in wastewater treatment systems is crucial for improving treatment performance. Supervised deep learning...

Machine Learning Analysis of Nutrient Associations with Peripheral Arterial Disease: Insights from NHANES 1999-2004.

BACKGROUND: Peripheral arterial disease (PAD) is a common manifestation of atherosclerosis, affecting over 200 million people worldwide. The incidence...

Design and optimization of tamarind seed polysaccharide-based scaffold for tissue engineering applications using statistical modeling and machine learning, and it's in-vitro validation.

This study explores the development and optimization of a novel biomaterial scaffold for tissue engineering, composed of Tamarind seed polysaccharide ...

Digital framework for georeferenced multiplatform surveillance of banana wilt using human in the loop AI and YOLO foundation models.

Bananas (Musa spp.) are a critical global food crop, providing a primary source of nutrition for millions of people. Traditional methods for disease m...

Comparative analysis of phytonutrients of leaves from South Africa and Nigeria, and their antimicrobial and antioxidant potentials by UPLC-ESI-QToF-MS and OPLS-DA chemometric analysis.

BACKGROUND: Lam. has bioactive phytonutrients in abundance and offers diverse health benefits. The leaves of this plant have established significance...

Application of machine learning and genomics for orphan crop improvement.

Orphan crops are important sources of nutrition in developing regions and many are tolerant to biotic and abiotic stressors; however, modern crop impr...

Prediction of stunting and its socioeconomic determinants among adolescent girls in Ethiopia using machine learning algorithms.

BACKGROUND: Stunting is a vital indicator of chronic undernutrition that reveals a failure to reach linear growth. Investigating growth and nutrition ...

Use of Machine Learning to Predict Individual Postprandial Glycemic Responses to Food Among Individuals With Type 2 Diabetes in India: Protocol for a Prospective Cohort Study.

BACKGROUND: Type 2 diabetes (T2D) is a leading cause of premature morbidity and mortality globally and affects more than 100 million people in the wor...

Prognosis modelling of adverse events for post-PCI treated AMI patients based on inflammation and nutrition indexes.

OBJECTIVE: This study aimed to evaluate the predictive performance of inflammatory and nutritional indices for adverse cardiovascular events (ACE) in ...

A machine learning-based model for predicting the risk of cognitive frailty in elderly patients on maintenance hemodialysis.

Elderly patients undergoing maintenance hemodialysis (MHD) face a heightened risk of cognitive frailty (CF), which significantly compromises quality o...

Predicting metabolite response to dietary intervention using deep learning.

Due to highly personalized biological and lifestyle characteristics, different individuals may have different metabolite responses to specific foods a...

Development and evaluation of interpretable machine learning regressors for predicting femoral neck bone mineral density in elderly men using NHANES data.

Osteoporotic femoral neck fractures (OFNFs) pose a significant orthopedic challenge in the elderly population, accounting for up to 40% of all osteopo...

Risk prediction models for feeding intolerance in patients with enteral nutrition: a systematic review and meta-analysis.

BACKGROUND: Although more risk prediction models are available for feeding intolerance in enteral-nourishment patients, it is still unclear how well t...

Risk prediction model of cognitive performance in older people with cardiovascular diseases: a study of the National Health and Nutrition Examination Survey database.

BACKGROUND AND AIM: Changes in cognitive function are commonly associated with aging in patients with cardiovascular diseases. The objective of this r...

Nutritional intelligence in the food system: Combining food, health, data and AI expertise.

Transformative change is needed across the food system to improve health and environmental outcomes. As food, nutrition, environmental and health data...

Diet Quality and Caloric Accuracy in AI-Generated Diet Plans: A Comparative Study Across Chatbots.

With the rise of artificial intelligence (AI) in nutrition and healthcare, AI-driven chatbots are increasingly recognised as potential tools for gene...

Improved health outcomes of nasopharyngeal carcinoma patients 3 years after treatment by the AI-assisted home enteral nutrition management.

OBJECTIVES: Patients with nasopharyngeal carcinoma (NPC) are prone to malnutrition, which leads to deterioration of health. This study is to clarify t...

Key risk factors of generalized anxiety disorder in adolescents: machine learning study.

Adolescents worldwide are increasingly affected by mental health disorders, with anxiety disorders, including Generalized Anxiety Disorder (GAD), bein...