A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients

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dc.contributor.authorOrbatu, Dilek
dc.contributor.authorBulgan, Zeynep Izem Peker
dc.contributor.authorOlmez, Emre
dc.contributor.authorEr, Orhan
dc.date.accessioned2025-03-20T09:50:26Z
dc.date.available2025-03-20T09:50:26Z
dc.date.issued2024
dc.departmentİzmir Bakırçay Üniversitesi
dc.description.abstractBackground: 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.
dc.description.sponsorshipIzmir Bakircay University Health Artificial Intelligence Studies Application and Research Centre; S.B.U. Izmir Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital
dc.description.sponsorshipWe would like to thank Izmir Bakircay University Health Artificial Intelligence Studies Application and Research Centre and S.B.U. Izmir Dr. Behcet Uz Pediatric Diseases and Surgery Training and Research Hospital for their support in this study. Also, we would like to thank all the participants.
dc.identifier.doi10.5812/ijp-148520
dc.identifier.issn2008-2142
dc.identifier.issn2008-2150
dc.identifier.issue2
dc.identifier.scopus2-s2.0-85217268304
dc.identifier.scopusqualityQ3
dc.identifier.urihttps://doi.org/10.5812/ijp-148520
dc.identifier.urihttps://hdl.handle.net/20.500.14034/2180
dc.identifier.volume35
dc.identifier.wosWOS:001418803700010
dc.identifier.wosqualityQ4
dc.indekslendigikaynakWeb of Science
dc.indekslendigikaynakScopus
dc.language.isoen
dc.publisherBrieflands
dc.relation.ispartofIranian Journal of Pediatrics
dc.relation.publicationcategoryMakale - Uluslararası Hakemli Dergi - Kurum Öğretim Elemanı
dc.rightsinfo:eu-repo/semantics/openAccess
dc.snmzKA_WOS_20250319
dc.subjectZinc Deficiency Adolescentsx
dc.subjectMachine Learning
dc.subjectDecision Support System
dc.titleA Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients
dc.typeArticle

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