Hybrid ensemble deep learning model for advancing breast cancer detection and classification in clinical applications.

Being the most common type of cancer worldwide, and affecting over 2.3 million women, breast cancer poses a significant health threat. Although survival rates have improved around the world due to advances in screening, diagnosis, and treatment, early detection remains crucial for effective management.

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.

The association between food preferences, eating behavior, and body weight among female university students in the United Arab Emirates.

Introduction: This cross-sectional study investigated the associations between lifestyle, eating habits, food preferences, consumption patterns, and obesity among female university students in the United Arab Emirates (UAE).

Methods: Approximately 4,728 participants, including both Emirati and Non-Emirati students (International Students). Data collection involved face-to-face interviews and anthropometric measurements, showing an interrelated relationship between food preferences and obesity among female university students.

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.

Hybrid Ensemble Deep Learning Model for Advancing Ischemic Brain Stroke Detection and Classification in Clinical Application.

Ischemic brain strokes are severe medical conditions that occur due to blockages in the brain's blood flow, often caused by blood clots or artery blockages. Early detection is crucial for effective treatment. This study aims to improve the detection and classification of ischemic brain strokes in clinical settings by introducing a new approach that integrates the stroke precision enhancement, ensemble deep learning, and intelligent lesion detection and segmentation models.

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.

Identification and prediction of association patterns between nutrient intake and anemia using machine learning techniques: results from a cross-sectional study with university female students from Palestine.

Purpose: This study utilized data mining and machine learning (ML) techniques to identify new patterns and classifications of the associations between nutrient intake and anemia among university students.

Methods: We employed K-means clustering analysis algorithm and Decision Tree (DT) technique to identify the association between anemia and vitamin and mineral intakes. We normalized and balanced the data based on anemia weighted clusters for improving ML models' accuracy.

Assessment of the vitamin D status and its determinants in young healthy students from Palestine.

The global prevalence of vitamin D deficiency is high. Poor vitamin D status, especially in women, has been reported in several countries in the Middle East despite adequate year-round sunlight for vitamin D synthesis. However, data on vitamin D status in Palestine are scarce.

Machine learning techniques for the identification of risk factors associated with food insecurity among adults in Arab countries during the COVID-19 pandemic.

Background: A direct consequence of global warming, and strongly correlated with poor physical and mental health, food insecurity is a rising global concern associated with low dietary intake. The Coronavirus pandemic has further aggravated food insecurity among vulnerable communities, and thus has sparked the global conversation of equal food access, food distribution, and improvement of food support programs. This research was designed to identify the key features associated with food insecurity during the COVID-19 pandemic using Machine learning techniques.

Machine learning techniques for identifying mental health risk factor associated with schoolchildren cognitive ability living in politically violent environments.

Introduction: Mental health and cognitive development are critical aspects of a child's overall well-being; they can be particularly challenging for children living in politically violent environments. Children in conflict areas face a range of stressors, including exposure to violence, insecurity, and displacement, which can have a profound impact on their mental health and cognitive development.

Methods: This study examines the impact of living in politically violent environments on the mental health and cognitive development of children.

Perspectives and practices of dietitians with regards to social/mass media use during the transitions from face-to-face to telenutrition in the time of COVID-19: A cross-sectional survey in 10 Arab countries.

During the COVID-19 pandemic, most healthcare professionals switched from face-to-face clinical encounters to telehealth. This study sought to investigate the dietitians' perceptions and practices toward the use of social/mass media platforms amid the transition from face-to-face to telenutrition in the time of COVID-19. This cross-sectional study involving a convenient sample of 2,542 dietitians (mean age = 31.

Status and correlates of food and nutrition literacy among parents-adolescents' dyads: findings from 10 Arab countries.

Background: Food literacy is capturing the attention worldwide and gaining traction in the Arab countries. Strengthening food and nutrition literacy among Arab teenagers are important promising empowering tools which can protect them from malnutrition. This study aims to assess the nutrition literacy status of adolescents with the food literacy of their parents in 10 Arab countries.

Antibiotic Resistance Knowledge, Attitudes, and Practices among Pharmacists: A Cross-Sectional Study in West Bank, Palestine.

Antibiotic resistance is an increasing problem worldwide. Dispensing antibiotics without prescription is a major contributing factor to antibiotic resistance. Pharmacists as healthcare providers are, in many studies, considered responsible for this practice.

Impact of COVID-19 lockdown on smoking (waterpipe and cigarette) and participants' BMI across various sociodemographic groups in Arab countries in the Mediterranean Region.

Introduction: Tobacco smokers are at high risk of developing severe COVID-19. Lockdown was a chosen strategy to deal with the spread of infectious diseases; nonetheless, it influenced people's eating and smoking behaviors. The main objective of this study is to determine the impact of the COVID-19 lockdown on smoking (waterpipe and cigarette) behavior and its associations with sociodemographic characteristics and body mass index.

Machine learning techniques for predicting depression and anxiety in pregnant and postpartum women during the COVID-19 pandemic: a cross-sectional regional study.

: Maternal depression and anxiety are significant public health concerns that play an important role in the health and well-being of mothers and children. The COVID-19 pandemic, the consequential lockdowns and related safety restrictions worldwide negatively affected the mental health of pregnant and postpartum women. This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety.

The Impact of COVID-19 on Physical (In)Activity Behavior in 10 Arab Countries.

Insufficient physical activity is considered a strong risk factor associated with non-communicable diseases. This study aimed to assess the impact of COVID-19 on physical (in)activity behavior in 10 Arab countries before and during the lockdown. A cross-sectional study using a validated online survey was launched originally in 38 different countries.

Dietary intake and lifestyle practices of eastern mediterranean postpartum women before and during COVID-19 pandemic: An internet-based cross-sectional survey.

Background: During the lockdown period, a substantial group of these women reported lifestyle changes.

Aim: The aim of the study is to characterize the dietary patterns, intake and the adherence to the United States Department of Agriculture (USDA) pregnancy guidelines before and during the COVID-19 pandemic in Eastern Mediterranean postartum women.

Methods: An internet-based cross-sectional survey was used to collect the data.

Assessment and Prediction of Depression and Anxiety Risk Factors in Schoolchildren: Machine Learning Techniques Performance Analysis.

Background: Depression and anxiety symptoms in early childhood have a major effect on children's mental health growth and cognitive development. The effect of mental health problems on cognitive development has been studied by researchers for the last 2 decades.

Objective: In this paper, we sought to use machine learning techniques to predict the risk factors associated with schoolchildren's depression and anxiety.

Cluster Analysis and Classification Model of Nutritional Anemia Associated Risk Factors Among Palestinian Schoolchildren, 2014.

Nutritional inadequacy has been a major health problem worldwide. One of the many health problems that result from it is anemia. Anemia is considered a health concern among all ages, particularly children, as it has been associated with cognitive and developmental delays.

Determinants of Exclusive Breastfeeding and Mixed Feeding Among Mothers of Infants in Dubai and Sharjah, United Arab Emirates.

Background: Breastfeeding (BF) is considered the ultimate method of infant feeding for at least the first 6 months of life. Exclusive breastfeeding (EBF) is one of the most effective interventions to improve child survival. The main objective of this study was to assess the prevalence and duration of exclusive breastfeeding and the associated factors among women in Dubai and Sharjah, UAE.

Assessment of Dietary and Lifestyle Responses After COVID-19 Vaccine Availability in Selected Arab Countries.

Background: The COVID-19 pandemic has been consistently associated with unhealthy lifestyle behaviors and dietary practices. This study aimed to assess the dietary and lifestyle behaviors of adults after COVID-19 vaccine availability and their attitude toward the vaccine in selected Arab countries.

Methods: A cross-sectional survey-based study was conducted between October 2021 and December 2021 using Google Forms ( = 2259).

Adherence to the United States Department of Agriculture Dietary Recommendations Pre- and During the Coronavirus Disease-19 Pandemic Among Pregnant Women in Arab Countries.

During pregnancy, woman's diet is one of the most preeminent factors affecting mother and child's health. Prior to the coronavirus disease-19 (COVID-19) pandemic, inadequate maternal diet and low adherence to dietary guidelines was reported among pregnant women in the Arab countries. Nowadays, COVID-19 infection during pregnancy is widely discussed among literature.