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

Aug 5, 2026

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Machine learning–based decision support was developed to identify zinc deficiency in adolescents aged 10–18 years, addressing substantial regional variability and the health impact of inadequate zinc intake. A cohort of 370 adolescents was analyzed using feature and output vectors with multiple classifiers. Model performance was evaluated with accuracy, precision, recall, and F1 score, where SVM achieved the strongest result. The proposed SVM-based system targets pediatric clinics by enabling automated risk detection from laboratory findings and supporting timely intervention to improve diagnostic efficiency and outcomes.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages12

Reading Time30 min

AccessFree to download

Abstract

Background

Objectives

Methods

Results

Conclusions

Keywords

Background

Regional zinc deficiency patterns

Proposed SVM-based decision support system

Iran J Pediatr. 2025 April; 35(2): e148520 [https://doi.org/10.5812/ijp-148520](https://doi.org/10.5812/ijp-148520) Published Online: 2024 October 26 Research Article A Decision Support System for Machine Learning-Based Determination of Zinc Deficiency: A Study in Adolescent Patients Dilek Orbatu 1 , * , Zeynep İzem Peker Bulğan 1 , Emre Olmez 2 , Orhan Er 3 1 İzmir Dr. Behçet Uz Pediatric Diseases and Surgery Training and Research Hospital, University of Health Sciences, İzmir, Turkey 2 Biomedical Engineering Department, İzmir Bakırçay University, İzmir, Turkey 3 Computers Engineering Department, İzmir Bakırçay University, İzmir, Turkey * Corresponding Author: İzmir Dr. Behçet Uz Pediatric Diseases and Surgery Training and Research Hospital, University of Health Sciences, İzmir, Turkey. Email: [drdilekorbatu@gmail.com](drdilekorbatu@gmail.com) Received: 5 May, 2024; Revised: 11 September, 2024; Accepted: 27 September, 2024 | | Abstract
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 10- 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 F1 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 quickly 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 F1 score balances precision and recall.
Results: The mean age of the patients in the dataset was 13.79 ± 1.18 years. Of the children, 64.32%(n = 238) were female and 35. 68%(n = 132) were male. It was found that 62.7%(n = 232) of the children had low zinc levels, while 37.3%(n = 138) 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 AI tools for adolescent health.
Conclusions: This study concluded that machine learning methods can

FAQs

What population does this study focus on for zinc deficiency detection?

The study analyzes children and adolescents aged 10–18 years to determine zinc deficiency risk.

Which machine learning models were evaluated?

Multiple classifiers were tested, including logistic regression, naive Bayes, CART, K-NN, SVM, gradient boosting, AdaBoost, bagging, random forest, MLP, and XGBoost.

What was the best-performing method and why is it useful?

SVM provided the highest classification accuracy (94.6%). An SVM-based system can automate high-risk detection from lab results and support early intervention alerts in pediatric settings.

Aug 5, 2026

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