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    A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients
    (Brieflands, 2024) Orbatu, Dilek; Bulgan, Zeynep Izem Peker; Olmez, Emre; Er, Orhan
    Background: Over the past three years, zinc deficiency among adolescents has varied based on region and access to healthcare. Globally, zinc deficiency affects approximately 2 billion people, leading to serious issues such as immune problems and growth delays, particularly in developing countries. In the U.S., around 10% of adolescents experienced zinc deficiency in 2021, with a higher prevalence among teenage girls. In Europe, deficiency rates are generally low but can be significant in Eastern Europe and Central Asia. In Asia, particularly in rural and low-income areas, deficiency rates range from 20- 30%. In Turkey, the prevalence is high due to poor nutrition. Objectives: This study aimed to develop a machine learning-based decision support system to determine zinc deficiency in children and adolescents aged ID- 18 years. Methods: This machine learning-based study was conducted with 370 adolescents aged 10-18 years to assess their zinc deficiency. The dataset consists of 8 feature vectors and an output vector. The machine learning methods used in the analysis include logistic regression, naive bayes, decision tree (CART), K-nearest neighbors (K-NN), support vector machine (SVM), gradient boosting classifier, AdaBoost classifier; bagging classifier; random forest classifier; multilayer perceptron (MLP) classifier; and XGBoost (XGB) classifier. Evaluation metrics such as accuracy, precision, recall, and Fl score were used to assess the performance of these methods. Including specific values for these metrics, such as SVM achieved 94.6% accuracy, would allow readers to quicldy compare the effectiveness of the models. Different metrics serve various purposes: Accuracy provides an overall view of performance, precision and recall highlight specific aspects, and the Fl score balances precision and recall. Results: The mean age of the patients in the dataset was 13.79 +/- 1.18 years. Of the children, 6432% (n = 238) were female and 35.68% (n =B2) were male. It was found that 62.7% (n = 232) of the children had low zinc levels, while 373% (n = ox ) did not require zinc supplementation. Thirteen different machine learning methods were applied to a 70% training and 30% testing set. As a result, the SVM method provided the most successful outcome with 94.6% accuracy. Implementing the SVM-based system in pediatric clinics could improve efficiency and patient care by automatically detecting high-risk zinc deficiency patients based on lab results, providing early intervention alerts for faster treatment, and improving health outcomes. Highlighting these practical applications could increase the study's appeal to healthcare professionals by demonstrating its real-world benefits. Providing detailed information on these applications would enhance the study's clarity and practical value, making it more valuable for researchers and healthcare providers interested in Al tools for adolescent health. Conclusions: This study concluded that machine learning methods can effectively determine zinc deficiency in children. The SVM method demonstrated superior classification performance compared to the other methods. An SVM-based decision support system could be integrated into pediatric outpatient clinics to enhance diagnostic accuracy and patient care.
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    The Covid-19 pandemic and Artificial Intelligence (AI) applications in health : how much are we interested in?
    (Dokuz Eylul Univ Inst Health Sciences, 2022) Öztop, Mehmet Burak; Pakdemirli, Ahu; Orbatu, Dilek; Erbaycu, Ahmet Emin; Özdemir, Senem Alkan; Başok, Banu; Bitim, Semih
    Purpose: New viruses have emerged, causing global damage and mass deaths that can spread to international borders, the latest of which is the new coronavirus (COVID-19). After the Second International Congress on Artificial Intelligence in Health, themed Artificial Intelligence in Health During COVID-19 Pandemic Process organized online by Izmir Bakircay University and Izmir Provincial Health Directorate with the contributions of the International Association of Artificial Intelligence in Health, a questionnaire was conducted to evaluate the knowledge of the participants about artificial intelligence applications.Material and Methods: This study aimed to evaluate the interest of the congress participants in this field with the questions which form the questionnaire such as the duration of the interest of the participants in the field of artificial intelligence in health, their publication status, the development of studies on artificial intelligence with the COVID-19 pandemic, demographic structures such as age and gender, and educational level. 130 participants answered the questionnaire consisting of 23 questions. Questionnaire responses were analyzed in a statistical setting.Results: We found that 130 people filled out the questionnaire and the majority of the participants were female, with participation from many organizations, but university staff showed more interest. We have seen that the 30-39 age group is more interested in artificial intelligence than the other age groups, but the majority of the participants do not have academic studies in this field. We found that the technical terms related to artificial intelligence were not well known by the participants, and that the number of participants who tended to this field, especially in the recent year, was high. Another important point was that people working in this field stated that they would definitely follow up if scientific activities continued.Conclusion: We know how important congresses, symposiums, courses and other meetings are, especially for scientist candidates, which will be held to raise awareness about the usage areas of artificial intelligence-based health technologies, to develop new communication and work networks by bringing together different disciplines, to create an agenda and to lay the groundwork for new studies, and we think that there is a need for many repetitive activities in this field and that these activities should be continued.