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IntroductionThis 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).MethodsApproximately 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.ResultsWhile sociodemographic factors and lifestyle habits contribute to obesity, this study uniquely focuses on the role of food preferences and food consumption patterns in body weight status. The findings reveal a significant correlation between the intake of high-sugar beverages–such as milk, juices, soft drinks, and energy drinks–and an increased risk of overweight and obesity among both Emirati and Non-Emirati populations. Notably, milk consumption was particularly associated with obesity in non-Emirati populations (F = 88.1, p

Abstract 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. Seven machine learning algorithms were used in the model, which used a dataset of 32 features. The model was designed to predict food insecurity across ten Arab countries in the Gulf and Mediterranean regions. A total of 13,443 participants were extracted from the international Corona Cooking Survey conducted by 38 different countries during the COVID -19 pandemic. Results The findings indicate that Jordanian, Palestinian, Lebanese, and Saudi Arabian respondents reported the highest rates of food insecurity in the region (15.4%, 13.7%, 13.7% and 11.3% respectively). On the other hand, Oman and Bahrain reported the lowest rates (5.4% and 5.5% respectively). Our model obtained accuracy levels of 70%-82% in all algorithms. Gradient Boosting and Random Forest techniques had the highest performance levels in predicting food insecurity (82% and 80% respectively). Place of residence, age, financial instability, difficulties in accessing food, and depression were found to be the most relevant features associated with food insecurity. Conclusions The ML algorithms seem to be an effective method in early detection and prediction of food insecurity and can profoundly aid policymaking. The integration of ML approaches in public health strategies could potentially improve the development of targeted and effective interventions to combat food insecurity in these regions and globally.

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. The average age of the children was 33 months, with 52% boys and 48% girls. Results: The analysis revealed that 18.1% of children faced food insecurity, with household education, family income, locality, district, and age emerging as significant determinants. Children from food-insecure environments exhibited lower average weight, height, and mid-upper arm circumference compared to their food-secure counterparts, indicating a direct correlation between food insecurity and reduced nutritional and growth metrics. Moreover, the machine learning models observed vitamin B1 as a key indicator of all forms of malnutrition, alongside vitamin K1, vitamin A, and zinc. Specific nutrients like choline in the “underweight” category and carbohydrates in the “wasting” category were identified as unique nutritional priorities. Conclusion: This study provides insights into the differential risks for growth issues among children, offering valuable information for targeted interventions and policymaking.

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. The proposed hybrid model was trained and tested using a dataset of 10,000 computed tomography scans. A 25-fold cross-validation technique was employed, while the model’s performance was evaluated using accuracy, precision, recall, and F1 score. The findings indicate significant improvements in accuracy for different stages of stroke images when enhanced using the SPEM model with contrast-limited adaptive histogram equalization set to 4. Specifically, accuracy showed significant improvement (from 0.876 to 0.933) for hyper-acute stroke images; from 0.881 to 0.948 for acute stroke images, from 0.927 to 0.974 for sub-acute stroke images, and from 0.928 to 0.982 for chronic stroke images. Thus, the study shows significant promise for the detection and classification of ischemic brain strokes. Further research is needed to validate its performance on larger datasets and enhance its integration into clinical settings.

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. The results indicated that food insecurity was significantly associated with dietary nutrient intake and sociodemographic factors, such as age, gender, income, and location. Indeed, 18.2% of the children were found to be food-insecure. A significant correlation was evidenced between inadequate consumption of various nutrients below the recommended dietary allowance and food insecurity. Specifically, insufficient protein, vitamin C, fiber, vitamin B12, vitamin B5, vitamin A, vitamin B1, manganese, and copper intake were found to have the highest rates of food insecurity. In addition, children residing in refugee camps experienced significantly higher rates of food insecurity. The findings emphasize the multilayered nature of food insecurity and its impact on children, emphasizing the need for personalized interventions addressing nutrient deficiencies and socioeconomic factors to improve children’s health and well-being.

IntroductionMental 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.MethodsThis study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10–15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age.ResultsThis study examines the impact of living in politically violent environments on the mental health and cognitive development of children. The analysis was conducted using machine learning techniques on the 2014 health behavior school children dataset, consisting of 6373 schoolchildren aged 10-15 from public and United Nations Relief and Works Agency schools in Palestine. The dataset included 31 features related to socioeconomic characteristics, lifestyle, mental health, exposure to political violence, social support, and cognitive ability. The data was balanced and weighted by gender and age.DiscussionThe findings can inform evidence-based strategies for preventing and mitigating the detrimental effects of political violence on individuals and communities, highlighting the importance of addressing the needs of children in conflict-affected areas and the potential of using technology to improve their well-being.

Background: The prevalence of dysglycemia among adolescents and younger children has been rising, yet health professionals are still unaware of the significance of this problem. According to the Palestinian Ministry of Health (MOH) records, most diabetic children under the age of 20 in Palestine are classified as type 1; nonetheless, very limited data are available for policymakers to frame cost-effective screening programs. This study aims to determine the prevalence of dysglycemia in a sample of obese and overweight Palestinian adolescents, identify risk factors associated with dysglycemia, and examine risk factors variance by gender. Methods: A cross-sectional sample of observed obese and overweight adolescents was selected from public schools in the Hebron governorate. Informed consent, physical examination, anthropometric, and laboratory tests (Random Blood Glucose Level (BGL) and fasting BGL ) were performed on a sample of 511 students (44.6% boys and 55.4% girls) aged 13–18-years (13-15 years =46.2% and 16-18 years =53.8%). Results: The prevalence of confirmed overweight and obese cases was 73.2%, and dysglycemia prevalence among the confirmed cases was 3.7% (5.3% boys and 2.5% girls). The BMI classifications of the dysglycemic children indicated that 42.1% were overweight and 31.1% were obese. Furthermore, 64.6% reported high systolic and/or diastolic blood pressure readings. Conclusions: The results of this study provide valuable information about the rising problem of dysglycemia among Palestinian adolescents and underlines the need for rapid screening programs and protocols for early detection and classification of the disease, leading to initiation of early prevention and treatment plans.

Background: The prevalence of dysglycemia among adolescents and younger children has been rising, yet health professionals are still unaware of the significance of this problem. According to the Palestinian Ministry of Health (MOH) records, most diabetic children under the age of 20 in Palestine are classified as type I; nonetheless, very limited data are available for policymakers to frame cost-effective screening programs. This study aims to determine the prevalence of dysglycemia in a sample of obese and overweight Palestinian children, identify risk factors associated with dysglycemia, and examine risk factors variance by gender. Methods: A cross-sectional sample of observed obese and overweight children was selected from public schools in the Hebron governorate. Informed consent, physical examination, anthropometric, and laboratory tests (Blood Glucose Level (BGL) and fasting BGL ) were performed on a sample of 511 students (44.6% boys and 55.4% girls) aged 13–18-years (13-15 years =46.2% and 16-18 years =53.8%). Results: The prevalence of confirmed overweight and obese cases was 73.2%, and dysglycemia prevalence among the confirmed cases was 3.7% (5.3% boys and 2.5% girls). The BMI classifications of the prediabetic children indicated that 42.1% were overweight and 31.1% were obese. Furthermore, 6.7% reported hypertension (both systolic and diastolic hypertension). Conclusions: The results of this study provide valuable information about the rising problem of dysglycemia among Palestinian children and underlines the need for rapid screening programs and protocols for early detection and classification of the disease, leading to initiation of early prevention and treatment plans.