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. Researchers have investigated the association between nutritional deficiencies and anemia through various methods. As novel analytical methods are needed to ascertain the association and reveal indirect ones, we aimed to classify nutritional anemia using the cluster analysis approach. In this study, we included 4,762 students aged between 10 and 17 years attending public and UNRWA schools in the West Bank. Students' 24-h food recall and blood sample data were collected for nutrient intake and hemoglobin analysis. The K-means cluster analysis was used to cluster the hemoglobin levels into two groups. Vitamin B12, folate, and iron intakes were used as the indicators of nutrient intake associated with anemia and were classified as per the Recommended Dietary Allowance (RDA) values. We applied the Classification and Regression Tree (CRT) model for studying the association between hemoglobin clusters and vitamin B12, folate, and iron intakes, sociodemographic variables, and health-related risk factors, accounting for grade and age. Results indicated that 46.4% of the students were classified into the low hemoglobin cluster, and 60.7, 72.5, and 30.3% of vitamin B12, folate, and iron intakes, respectively, were below RDA. The CRT analysis indicated that vitamin B12, iron, and folate intakes are important factors related to anemia in girls associated with age, locality, food consumption patterns, and physical activity levels, while iron and folate intakes were significant factors related to anemia in boys associated with the place of residence and the educational level of their mothers. The deployment of clustering and classification techniques for identifying the association between anemia and nutritional factors might facilitate the development of nutritional anemia prevention and intervention programs that will improve the health and wellbeing of schoolchildren.
Irreversible electroporation is a novel tissue ablation technique which entails delivering intense electrical pulses to target tissue, hence producing fatal defects in the cell membrane. The present ...study numerically analyzes the potential impact of liver blood vessels on ablation by irreversible electroporation because of their influence on the electric field distribution. An anatomically realistic computer model of the liver and its vasculature within an abdominal section was employed, and blood vessels down to 0.4 mm in diameter were considered. In this model, the electric field distribution was simulated in a large series of scenarios (N = 576) corresponding to plausible percutaneous irreversible electroporation treatments by needle electrode pairs. These modeled treatments were relatively superficial (maximum penetration depth of the electrode within the liver = 26 mm) and it was ensured that the electrodes did not penetrate the vessels nor were in contact with them. In terms of total ablation volume, the maximum deviation caused by the presence of the vessels was 6%, which could be considered negligible compared to the impact by other sources of uncertainty. Sublethal field magnitudes were noticed around vessels covering volumes of up to 228 mm3. If in this model the blood was substituted by a liquid with a low electrical conductivity (0.1 S/m), the maximum volume covered by sublethal field magnitudes was 3.7 mm3 and almost no sublethal regions were observable. We conclude that undertreatment around blood vessels may occur in current liver ablation procedures by irreversible electroporation. Infusion of isotonic low conductivity liquids into the liver vasculature could prevent this risk.
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.
This 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.
This 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.
The 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.
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.
A cross-sectional study was conducted in four hospitals and four healthcare centers in Dubai and Sharjah between September 2017 and December 2017. Hospitals and centers are governmental and provide maternal and child health services. A convenience sample of 858 Arab and Emirati mothers with children under the age of 2 years participated in the study. Face-to-face interviews were conducted by using structured questionnaires. The study was approved by the University Ethical Committee and the UAE Ministry of Health before data collection. Descriptive statistics were computed to describe all the questionnaire items. The chi-square test was used to compare the study's categorical variables. A binary logistic regression analysis was used to predict the relationship between BF and its associated factors. Statistical tests with
-values < 0.05 were considered statistically significant.
The mean age of the participating mothers was 30.6 (SD 5.5) years. Results showed that the prevalence of exclusive breastfeeding among the study participants was 24.4% (31.1% in Sharjah and 22% in Dubai;
= 0.003). The binary logistic regression reported that mother's and father's education, skin-to-skin period, number of children, mothers' health, and place of living were significantly associated with exclusive breastfeeding (
< 0.05). The results reported a significant association between EB and duration of breastfeeding (OR = 6.9,
= 0.002), husband education (OR = 2.1,
= 0.015), mother education (OR = 1.3,
= 0.027), number of children (OR = 7.9,
= 0.045), having any health problem (OR = 1.2,
= 0.045), and living place (OR = 1.4,
= 0.033), and a non-significant positive effect of family size and family income. Furthermore, the result reported a significant association between mixed breastfeeding and duration of breastfeeding (OR = 0.1,
= 0.000), skin-to-skin period (OR = 0.3,
= 0.002), underweight (OR = 4.7,
= 0.034), last infant's sex (OR = 1.6,
= 0.010), having maid at home (OR = 2.1,
= 0.000), number of children (OR = 0.2,
= 0.013), and living place (OR =1.1,
= 0.014), and a non-significant association with family size and family income.
Therefore, a health promotion program for exclusive breastfeeding during antenatal health visits, together with initiating health policies in maternal hospitals to encourage the initiation of breastfeeding during the first hour of birth and the introduction of skin-to-skin contact during the first 5 min of birth are highly recommended.
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.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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: 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.
Methods: This regional study aimed to develop a machine learning (ML) model for the prediction of maternal depression and anxiety. The study used a dataset collected from five Arab countries during the COVID-19 pandemic between July to December 2020. The population sample included 3569 women (1939 pregnant and 1630 postpartum) from five countries (Jordan, Palestine, Lebanon, Saudi Arabia, and Bahrain). The performance of seven machine learning algorithms was assessed for the prediction of depression and anxiety symptoms.
Results: The Gradient Boosting (GB) and Random Forest (RF) models outperformed other studied ML algorithms with accuracy values of 83.3% and 83.2% for depression, respectively, and values of 82.9% and 81.3% for anxiety, respectively. The Mathew's Correlation Coefficient was evaluated for the ML models; the Naïve Bayes (NB) and GB models presented the highest performance measures (0.63 and 0.59) for depression and (0.74 and 0.73) for anxiety, respectively. The features' importance ranking was evaluated, the results showed that stress during pregnancy, family support, financial issues, income, and social support were the most significant values in predicting anxiety and depression.
Conclusion: Overall, the study evidenced the power of ML models in predicting maternal depression and anxiety and proved to be an efficient tool for identifying and predicting the associated risk factors that influence maternal mental health. The deployment of machine learning models for screening and early detection of depression and anxiety among pregnant and postpartum women might facilitate the development of health prevention and intervention programs that will enhance maternal and child health in low- and middle-income countries.
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.
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