What drives weight status among female university students? - A machine learning analysis of sociodemographic, dietary, and lifestyle determinants

Q: What is the main research question of the study?

The study asks which key factors influence weight status among female university students and how accurately machine learning models can identify them.

Q: How was the dataset analyzed in this research?

The cross-sectional study analyzed data from 7,092 female university students aged 18–30 years using machine learning models such as Random Forest, SVM, logistic regression, gradient boosting, decision trees, and ensemble methods.

Q: What were the leading drivers of different weight categories?

Underweight was linked to low water/milk intake and fast-food preference, overweight to added oil, larger eating quantity, and low physical activity, and obesity to energy drink consumption, salty snacks, and irregular meals.

Aug 4, 2026

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Obesity and underweight are increasingly prevalent among young adult women, shaped by diet, lifestyle, and socioeconomic conditions that interact in complex ways. This study applies machine learning to identify key determinants of weight status among female university students and to evaluate how accurately models can detect patterns across underweight, overweight, and obesity categories. A cross-sectional analysis of 7,092 students in Palestine and the UAE tests multiple algorithms and assesses performance via cross-validation.

LanguageEnglish

CategoryResearch & Report

FormatPDF

Pages13

Reading Time33 min

AccessFree to download

Background

Methods

Results

Conclusion

Keywords

OPEN ACCESS EDITED BY Gianpiero Greco, University of Bari Aldo Moro, Italy REVIEWED BY Fentaw Wassie Feleke, Woldia University, Ethiopia Haoxian Tang, First Affiliated Hospital of Shantou University Medical College, China Viral Ishvarlal Champaneri, Zydus Medical College and Hospital, India *CORRESPONDENCE Radwan Qasrawi [radwan@staff.alquds.edu](radwan@staff.alquds.edu)[ ](radwan@staff.alquds.edu)Haleama Al Sabbah [haleemah.alsabah@adu.ac.ae](haleemah.alsabah@adu.ac.ae)[ ](haleemah.alsabah@adu.ac.ae)RECEIVED 07 April 2025 ACCEPTED 26 June 2025 PUBLISHED 18 July 2025 CITATION Qasrawi R, Ajab A, Cheikh Ismail L, Al Dhaheri A, Alblooshi S, Abu Ghoush R, Vicuna Polo S, Amro M, Thwib S, Issa G and Al Sabbah H (2025) What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants. Front. Nutr. 12:1574063 . doi: 10.3389/fnut.2025.1574063 COPYRIGHT © 2025 Qasrawi, Ajab, Cheikh Ismail, Al Dhaheri, Alblooshi, Abu Ghoush, Vicuna Polo, Amro, Thwib, Issa and Al Sabbah. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY) . The use, distribution or reproduction in other forums is permitted, provided the original author(s) and the copyright owner(s) are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms. TYPE Original Research PUBLISHED 18 July 2025 DOI 10.3389/fnut.2025.1574063 What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants Radwan Qasrawi 1,2*, Abir Ajab3, Leila Cheikh Ismail3,4, Ayesha Al Dhaheri 5, Sharifa Alblooshi 6, Razan Abu Ghoush 1, Stephanny Vicuna Polo 1, Malak Amro 1, Suliman Thwib 1, Ghada Issa 1 and Haleama Al Sabbah7* 1 Department of Computer Science, Al-Quds University, Jerusalem, Palestine, 2 Department of Computer Engineering, Istinye University, Istanbul, Türkiye, 3 Department of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab Emirates, 4 Nuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United Kingdom, 5 Department of Nutrition and Health, College of Medicine and Health Sciences, UAE University, Al Ain, United Arab Emirates, 6 Department of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab Emirates, 7 Department of Public Health, College of Health Sciences, Abu Dhabi University, Abu Dhabi, United Arab Emirates Background: 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 the chi-square test (p <

FAQs

What is the main research question of the study?

The study asks which key factors influence weight status among female university students and how accurately machine learning models can identify them.

How was the dataset analyzed in this research?

The cross-sectional study analyzed data from 7,092 female university students aged 18–30 years using machine learning models such as Random Forest, SVM, logistic regression, gradient boosting, decision trees, and ensemble methods.

What were the leading drivers of different weight categories?

Underweight was linked to low water/milk intake and fast-food preference, overweight to added oil, larger eating quantity, and low physical activity, and obesity to energy drink consumption, salty snacks, and irregular meals.

Aug 4, 2026

Upload by Eden