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Öğe Hybrid ensemble deep learning model for advancing ischemic brain stroke detection and classification in clinical application(MDPI, 2024) Qasrawi, Radwan; Qdaih, Ibrahem; Daraghmeh, Omar; Thwib, Suliman; Polo, Stephanny Vicuna; Atari, Siham; Abu Al-Halawa, DialaIschemic 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.Öğe Investigating the association between nutrient intake and food insecurity among children and adolescents in palestine using machine learning techniques(MDPI, 2024) Qasrawi, Radwan; Sgahir, Sabri; Nemer, Maysaa; Halaikah, Mousa; Badrasawi, Manal; Amro, Malak; Vicuna Polo, Stephanny; Abu Al-Halawa, Diala; Mujahed, Doa’a; Nasreddine, Lara; Elmadfa, Ibrahim; Atari, SihamFood 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.Öğe Machine learning approach for predicting the impact of food insecurity on nutrient consumption and malnutrition in children aged 6 months to 5 years(MDPI, 2024) Qasrawi, Radwan; Sgahir, Sabri; Nemer, Maysaa; Halaikah, Mousa; Badrasawi, Manal; Amro, Malak; Polo, Stephanny Vicuna; Abu Al-Halawa, Diala; Mujahed, Doa'a; Nasreddine, Lara; Elmadfa, Ibrahim; Atari, Siham; Al-Jawaldeh, AyoubBackground: 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.Öğe The association between food preferences, eating behavior, and body weight among female university students in the United Arab Emirates(Frontiers media, 2024) Al Sabbah, Haleama; Ajab, Abir; Ismail, Leila Cheikh; Al Dhaheri, Ayesha; Alblooshi, Sharifa; Atari, Siham; Polo, Stephanny Vicuna; Amro, Malak; Qasrawi, RadwanIntroduction: 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. Results: While 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 < 0.001) and with overweight status in Non-Emiratis (F = 7.73, p < 0.05). The consumption of juices and soft drinks was linked to obesity. Additionally, a significant preference for fruits and vegetables among overweight and obese students was observed, indicating a trend toward healthier food choices. However, there was also a clear preference for high-calorie, low-nutrient foods such as processed meats, sweets, and salty snacks. Fast food items like burgers, fried chicken, fries, pizza, shawarma, chips, and noodles were significantly correlated with increased body weight status, especially shawarma, which showed a notably high correlation with both obesity and overweight statuses (F-values of 38.3 and 91.11, respectively). Conclusion: The study indicated that food choices shape weight-related outcomes is important for designing effective strategies to promote healthier dietary patterns.
