Factors associated with malnutrition inflammation score among hemodialysis patients: A cross-sectional investigation in tertiary care hospital, Palestine.

Malnutrition is a prevalent complication in hemodialysis patients and is associated with increased mortality and morbidity. This study aimed to identify the risk factors associated with malnutrition among hemodialysis patients including patient's general characteristics, functional status, and dietary intake. This study involved hemodialysis patients in An-Najah National University Hospital at Nablus/Palestine.

Development and validation of food frequency questionnaire screener software for sodium intake among palestinian population.

Background: High sodium intake has been linked to negative health effects, including cardiovascular and renal diseases. Traditional dietary sodium assessment methods are time-consuming and subjected to errors. Using technology may increase the efficiency and accuracy of dietary assessment.

Intradialytic eating practices and health outcomes among hemodialysis patients, cross-sectional study.

Background And Aims: Intradialytic eating practices is a subject of debate among hemodialysis patients and is associated with a variety of clinical implications. This study aimed to investigate eating practices during hemodialysis and their influence on health outcome, including various symptoms experienced during dialysis, intradialytic hypotension, dialysis adequacy, and malnutrition.

Methods: A cross-sectional study was conducted on hemodialysis patients.

Machine Learning Approach for Predicting the Impact of Food Insecurity on Nutrient Consumption and Malnutrition in Children Aged 6 Months to 5 Years.

Background: Food insecurity significantly impacts children's health, affecting their development across cognitive, physical, and socio-emotional dimensions. This study explores the impact of food insecurity among children aged 6 months to 5 years, focusing on nutrient intake and its relationship with various forms of malnutrition.

Methods: Utilizing machine learning algorithms, this study analyzed data from 819 children in the West Bank to investigate sociodemographic and health factors associated with food insecurity and its effects on nutritional status.

Investigating the Association between Nutrient Intake and Food Insecurity among Children and Adolescents in Palestine Using Machine Learning Techniques.

Food insecurity is a public health concern that affects children worldwide, yet it represents a particular burden for low- and middle-income countries. This study aims to utilize machine learning to identify the associations between food insecurity and nutrient intake among children aged 5 to 18 years. The study's sample encompassed 1040 participants selected from a 2022 food insecurity household conducted in the West Bank, Palestine.