Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 55 2 2026 10 04 41265 41265 EN Ahmet Urhan Tokat Gaziosmanpaşa University, Artova Vocational College, Department of Therapy and Rehabilitation, Tokat, Turkey Mustafa Ozturk Tokat Gaziosmanpaşa University, Artova Vocational School, Department of Veterinary, Tokat, Turkey Ahmet Depreli Forensic Medicine Institute, Konya Forensic Medicine Branch Directorate, Konya, Turkey Omer Nasip Tokat Gaziosmanpaşa University, Faculty of Medicine, Tokat, Turkey Tugba Ataseven Ordu State Hospital, Ordu, Turkey 2025 10 25 2026 02 01 Background: This study aims to evaluate the predictability of individuals’ Body Mass Index (BMI) and sex by integrating anthropometric data and balance parameters into machine learning algorithms. The primary goal is to propose a faster, more reliable, and data-driven model compared to traditional statistical approaches. Additionally, the effects of BMI, height, and weight variables on balance performance were analyzed. Materials and Methods: This study included 210 healthy adults (105 males, 105 females) aged 18–25 years. Height and weight were measured following standard anthropometric protocols, and balance performance was evaluated via the single-leg stance test on both sides. Anthropometric parameters (height, weight, dominant side) and balance durations were analyzed using machine learning algorithms (Decision Tree, Random Forest, Support Vector Machine). Model accuracy, precision, recall, and F1-score were calculated through 10-fold cross-validation. Statistical analyses were performed with IBM SPSS Statistics 25. Results: Machine learning algorithms achieved high accuracy rates in predicting both BMI and sex. The Random Forest model demonstrated the best overall performance, reaching an accuracy of 84% for sex prediction and 98% for BMI prediction. Furthermore, our results revealed that BMI and body weight negatively affected balance performance, whereas height was not identified as a significant determinant. Conclusion: The integration of anthropometric and balance parameters with machine learning algorithms provides an effective and non-invasive approach for predicting BMI and sex. This hybrid biomechanical–computational model may serve as a promising tool in personalized health assessments, balance analysis, and early risk detection studies. https://ijph.tums.ac.ir/index.php/ijph/article/view/41265

Tehran University of Medical Sciences Iranian Journal of Public Health 2251-6085 55 2 2026 10 04 38737 38737 Hamid Adeli Fard Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Maryam Khosravi Shahid Dr. Beheshti hospital laboratory department, Sabzevar University of Medical Sciences: Sabzevar,Iran Ali Bahari Department of Internal Medicine, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Mahboobeh Ghasemzadeh Rahbardar Clinical Research Development Unit, Shahid Hasheminejad Hospital, Mashhad University of Medical Sciences, Mashhad, Iran Habibollah Esmaily Social Determinants of Health Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Reza Rezvani Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Farahnaz Akrami Moghadam Department of Nutrition, Faculty of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran Mahsa Amarlou Department of Nutrition Sciences, Varastegan Institute for Medical Sciences, Mashhad, Iran Gordon Ferns Brighton & Sussex Medical School, Division of Medical Education, Falmer, Brighton, Sussex BN1 9PH, UK Maryam Saberi Karimian Lung Diseases Research Center, Mashhad University of Medical Sciences, Mashhad, Iran Majid Ghayour-Mobarhan Metabolic Syndrome research center, School of Medicine, Mashhad University of Medical Sciences, Mashhad, Iran 2025 04 05 2026 02 01 Background: The phase angle (PA) serves as an important indicator of cell membrane health and integrity, with higher values reflecting enhanced cell function. While malnutrition and the prognosis of chronic diseases have been extensively assessed, exploring the relationship between PA and Non-Alcoholic Fatty Liver Disease (NAFLD) has the potential to influence future disease management approaches. Aims: This study aimed to compare PA values across different NAFLD grades and investigate the correlation between anthropometric indices and phase angle measurements. Methods: A cross-sectional investigation was carried out on 191 people with NAFLD and 180 healthy controls. Anthropometric measurements were taken in the baseline using the Bio-Impedance Analyzer (In Body 770, In Body, South Korea), and hepatic steatosis was examined using FibroScan and ultrasound. The data was analyzed using SPSS version 16. Results: There was a significant unadjusted difference in trunk PA and whole-body PA between case and control groups (p< 0.001 and p< 0.05, respectively). After adjustment for confounders, the difference between different grades of fatty liver determined by ultrasound showed a significant difference (p< 0.001). Arm circumference emerged as the most positively correlated index influencing PA in both groups (p< 0.001), while the ratio of extracellular water to total body water exhibited the most negative correlation with PA (p< 0.001). Conclusions: The study found a substantial difference in PA levels between NAFLD patients and healthy individuals. Further research is needed to determine the potential of PA as a sensitive and specific predictor of NAFLD. https://ijph.tums.ac.ir/index.php/ijph/article/view/38737