What Drives Weight Status Among Female University Students? A Machine Learning Analysis of Sociodemographic, Dietary, and Lifestyle Determinants

Radwan Qasrawi^1,2*Abir Ajab³Leila Ismail^3,4Ayesha Al Dhaheri⁵Sharifa Alblooshi⁶Razan Abu Ghoush¹Stephanny Vicuna Polo¹Malak Amro¹Suliman Thwib¹Ghada Issa¹Haleama Al Sabbah⁷

• ¹Al-Quds University, Jerusalem, Palestine
• ²University of Istinye, Istanbul, Türkiye
• ³University of Sharjah, Sharjah, United Arab Emirates
• ⁴University of Oxford, Oxford, England, United Kingdom
• ⁵United Arab Emirates University, Al-Ain, Abu Dhabi, United Arab Emirates
• ⁶Zayed University, Abu Dhabi, United Arab Emirates
• ⁷Abu Dhabi University, Abu Dhabi, United Arab Emirates

Obesity and underweight are increasingly common among young adult women, often resulting from complex interactions between diet, lifestyle, and socioeconomic factors. This study addresses that gap by applying machine learning to a wide range of behavioral, dietary, and demographic data. The main research question asks: what are the key factors influencing weight status among female university students, and how accurately can machine learning models identify them? We hypothesize that different factors are significantly associated with underweight, overweight, and obesity, and that machine learning can reliably detect these patterns.The aim is to identify the strongest predictors and support more targeted weight management strategies.Methods: This cross-sectional study analyzed data from 7,092 female university students (aged 18-30 years) in Palestine and the UAE. Sociodemographic, dietary, and lifestyle predictors were evaluated using machine learning (Random Forest, SVM, Logistic Regression, Gradient Boosting, Decision Trees, and ensemble methods). Synthetic Minority Over-sampling (SMOTE) addressed class imbalance. Model performance was assessed via 10-fold cross-validation, with significance determined by Chi-square test (p < 0.05, 95% CI).Results: The Random Forest model achieved the highest accuracy (obesity: 96.8%, underweight: 94.6%, overweight: 90.3%) and AUC (0.95-0.97). Key determinants varied by weight status: underweight was associated with low water/milk intake and preference for fast food; overweight with added oil, large eating quantity, and low physical activity; and obesity with energy drink consumption, salty snacks, and irregular meals. All findings were statistically significant (p < .001). Socio-demographic factors (e.g., low income, marital status) and lifestyle habits (e.g., sleep <5 hours, fast eating) were also significantly related to weight status.The integration of these findings into weight management frameworks can significantly enhance the detection and understanding of modifiable determinants, thereby informing public health interventions, guiding the development of targeted weight management strategies, and contributing to the global movement toward healthier bodies.