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. Methods: The study sample consisted of 3984 students in fifth to ninth grades, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors schoolchildren questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. We used 5 machine learning techniques (random forest [RF], neural network, decision tree, support vector machine [SVM], and naive Bayes) for prediction. Results: The results indicated that the SVM and RF models had the highest accuracy levels for depression (SVM: 92.5%; RF: 76.4%) and anxiety (SVM: 92.4%; RF: 78.6%). Thus, the SVM and RF models had the best performance in classifying and predicting the students’ depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting the depression and anxiety scales. Conclusions: Overall, machine learning proved to be an efficient tool for identifying and predicting the associated factors that influence student depression and anxiety. The machine learning techniques seem to be a good model for predicting abnormal depression and anxiety symptoms among schoolchildren, so the deployment of machine learning within the school information systems might facilitate the development of health prevention and intervention programs that will enhance students’ mental health and cognitive development.

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.

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.

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.

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.