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    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, Siham
    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.
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    Machine learning techniques for tomato plant diseases clustering, prediction and classification
    (IEEE, 2021) Qasrawi, Radwan; Amro, Malak; Zaghal, Raid; Sawafteh, Mohammad; Vicuna Polo, Stephanny
    The agriculture sector in Palestine faces several challenges that affect the quality of crop yields, including plant diseases. Plant diseases may be caused by bacteria, viruses, and fungus, among others. Early detection and classification of these diseases allow farmers to reduce the instances and control the effect that the disease may have on their crops. Therefore, this study utilizes machine learning models for the clustering, prediction, and classification of tomato plant diseases in Palestine. The study used 3000 smartphone digital images of five tomato plant diseases (Alternaria Solani; Botrytis Cinerea; Panonychus Citri; Phytophthora Infestans; Tuta Absoluta) collected from three districts across the West Bank (Tulkarem, Jenin, and Tubas). The machine learning models used image embedding and hierarchical clustering techniques in clustering, and the neural network, random Forest, naïve Bayes, SVM, Decision Tree, and Logistic regression for prediction and classification. The models’ accuracy was validated in reference to a tomato plant diseases database created by plant pathogens experts. The clustering model provided 7 diseases clustering with an accuracy rate of 70%, while the neural network and logistic regression models reported performance accuracies of 70.3% and 68.9% respectively. The findings demonstrate that the proposed approach provides acceptable accuracy rates in the clustering, detection, and classification of tomato plant disease. Thus, the deployment of machine learning techniques in the agriculture sector might help Palestinian farmers better manage and control tomato diseases.
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    Schoolchildren' depression and anxiety risk factors assessment and prediction: Machine learning techniques performance analysis
    (JMIR, 2022) Qasrawi, Radwan; Vicuna Polo, Stephanny; Al-Halawa, Diala Abu; Hallaq, Sameh; Abdeen, Ziad
    Background: Depression and anxiety symptoms in early childhood have a major effect on children's mental health growth and cognitive development. Studying the effect of mental health problems on cognitive development has gained researchers' attention for the last two decades. Objective: In this paper, we seek to use machine learning techniques to predict the risk factors associated with school children's depression and anxiety. Methods: The study data consisted of 5685 students in grades 5-9, aged 10-15 years, studying at public and refugee schools in the West Bank. The data were collected using the health behaviors school children questionnaire in the 2013-2014 academic year and analyzed using machine learning to predict the risk factors associated with student mental health symptoms. Five machine learning techniques (Random Forest, Neural Network, Decision Tree, Support Vector Machine, and Naïve Bayes) were used for prediction. Results: The results indicated that the SVM and Random Forest model had the highest accuracy levels (SVM= 92.5%, RF=76.4%; SVM=92.4%, RF=78.6%) for depression and anxiety respectively. Thus, the SVM and Random Forest had the best performance in classifying and predicting the student's depression and anxiety. The results showed that school violence and bullying, home violence, academic performance, and family income were the most important factors affecting 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 ML 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.