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. 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.

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. 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.

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. 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.

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. 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. Results: 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. Discussion: 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.

The applicability of Boston Carpal Tunnel Questionnaire as a screening tool for carpal tunnel syndrome among potential high-risk female population in the West Bank: a cross-sectional study.

Carpal tunnel syndrome (CTS) is an entrapment neuropathy with a high level of morbidity if neglected. Boston Carpal Tunnel Questionnaire (BCTQ) was designed to track patients' progress after diagnosis. However, few studies showed that this questionnaire might be applicable as a screening tool for CTS. Objective: This study aims to identify the ability of BCTQ to detect symptoms and functional limitations of CTS among the potential high-risk population. Materials and Methods: This study is a cross-sectional study involving 366 females, aged 30-60 years, residents of the West Bank, Palestine. Data was collected using BCTQ to assess participants' symptoms severity and functional limitations. Results: Symptoms were reported in 72.4% of participants, while functional limitations were reported in 64.2%. Very severe symptoms were found in 1.1% of the study population, and very severe functional limitations were reported in 1.4% only. BCTQ reliability testing via Cronbach alpha showed a score of 0.937 and 0.922 for symptom severity and functional limitations scales, respectively. The most common reported symptom was pain during the daytime, while the 'household chores' was the most common functional limitation. Conclusion: This study showed that many participants reported symptoms and functional limitations of CTS without a prior diagnosis. The BCTQ can potentially be used as a screening tool for middle-aged females in the West Bank, Palestine, as it showed strong applicability. However, this study could not compute the actual prevalence of CTS due to the lack of access to clinical and electrophysiological confirmation.

Extreme Oncoplastic Breast Surgery: A case series of three patients from a lower-middle income country.

Introduction: Oncoplastic breast surgery has become a major player in modern breast surgery.It broadens the indications for breast-conserving surgery. More challenging cases are being treated more with what so-called "extreme oncoplastic breast surgery" which is defined as a breast-conserving operation, using oncoplastic techniques, in a patient who, in most physicians' opinions, requires a mastectomy. Methods: Replacement and/or displacement oncoplastic techniques with contralateral symmetrization to three female patients with breast cancer were done by an oncoplastic breast surgeon. Outcomes: The three patients had smooth recoveries with good aesthetic,oncologic and psychological outcomes. Conclusion: Oncoplastic breast surgery can be a better option than mastectomy with good oncologic, Psychological and aesthetic outcomes, even with extreme cases, yet long-term studies are needed.

Sex disparities in food consumption patterns, dietary diversity and determinants of self-reported body weight changes before and amid the COVID-19 pandemic in 10 Arab countries.

Background: The COVID-19 pandemic along with its confinement period boosted lifestyle modifications and impacted women and men differently which exacerbated existing gender inequalities. The main objective of this paper is to assess the gender-based differentials in food consumption patterns, dietary diversity and the determinants favoring weight change before and amid the COVID-19 pandemic among Arab men and women from 10 Arab countries. Methods: A cross-sectional study was conducted based on a convenience sample of 12,447 households' family members (mean age: 33.2 ± 12.9; 50.1% females) and information from participants aged 18 years and above was collected about periods before and during the pandemic. Results: Findings showed that, during the COVID-19 period, the dietary diversity, declined by 1.9% among females compared to males (0.4%) (p < 0.001) and by 1.5% among overweight participants (p < 0.001) compared to their counterparts. Conclusions: To conclude, gender-sensitive strategies and policies to address weight gain and dietary diversity during emergent shocks and pandemics are urgently needed in the region.

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

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.

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. 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.

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. 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.