{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,3]],"date-time":"2026-03-03T06:29:15Z","timestamp":1772519355449,"version":"3.50.1"},"reference-count":46,"publisher":"Frontiers Media SA","license":[{"start":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T00:00:00Z","timestamp":1752796800000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":["frontiersin.org"],"crossmark-restriction":true},"short-container-title":["Front. Nutr."],"abstract":"<jats:sec><jats:title>Background<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec><jats:sec><jats:title>Methods<\/jats:title><jats:p>This cross-sectional study analyzed data from 7,092 female university students (aged 18\u201330\u202fyears) 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 (<jats:italic>p<\/jats:italic> &amp;lt;\u202f0.05, 95% CI).<\/jats:p><\/jats:sec><jats:sec><jats:title>Results<\/jats:title><jats:p>The Random Forest model achieved the highest accuracy (obesity: 96.8%, underweight: 94.6%, overweight: 90.3%) and AUC (0.95\u20130.97). The main drivers of weight status categories were as follows: 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 (<jats:italic>p<\/jats:italic> &amp;lt;\u202f0.001). Socio-demographic factors (e.g., low income and marital status) and lifestyle habits (e.g., sleep &amp;lt;5\u202fh and fast eating) were also significantly related to weight status.<\/jats:p><\/jats:sec><jats:sec><jats:title>Conclusion<\/jats:title><jats:p>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.<\/jats:p><\/jats:sec>","DOI":"10.3389\/fnut.2025.1574063","type":"journal-article","created":{"date-parts":[[2025,7,18]],"date-time":"2025-07-18T04:11:43Z","timestamp":1752811903000},"update-policy":"https:\/\/doi.org\/10.3389\/crossmark-policy","source":"Crossref","is-referenced-by-count":1,"title":["What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants"],"prefix":"10.3389","volume":"12","author":[{"given":"Radwan","family":"Qasrawi","sequence":"first","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Abir","family":"Ajab","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Leila","family":"Cheikh Ismail","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ayesha","family":"Al Dhaheri","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Sharifa","family":"Alblooshi","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Razan","family":"Abu Ghoush","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Stephanny","family":"Vicuna Polo","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Malak","family":"Amro","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Suliman","family":"Thwib","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Ghada","family":"Issa","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Haleama","family":"Al Sabbah","sequence":"additional","affiliation":[],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"1965","published-online":{"date-parts":[[2025,7,18]]},"reference":[{"key":"ref1","doi-asserted-by":"publisher","first-page":"514674","DOI":"10.1155\/2010\/514674","article-title":"Global and national socioeconomic disparities in obesity, overweight, and underweight status","author":"Moore","year":"2010","journal-title":"J Obes"},{"key":"ref2","doi-asserted-by":"publisher","DOI":"10.1111\/obr.13123","article-title":"Global prevalence of obesity, overweight and underweight in children, adolescents and adults with autism spectrum disorder, attention-deficit hyperactivity disorder: a systematic review and meta-analysis","volume":"21","author":"Li","year":"2020","journal-title":"Obes Rev"},{"key":"ref3","doi-asserted-by":"publisher","first-page":"100901","DOI":"10.1016\/j.imu.2022.100901","article-title":"Estimation of obesity levels based on dietary habits and condition physical using computational intelligence","volume":"29","author":"Santisteban Quiroz","year":"2022","journal-title":"Inform Med Unlocked"},{"key":"ref4","doi-asserted-by":"publisher","first-page":"e0183934","DOI":"10.1371\/journal.pone.0183934","article-title":"Prevalence of overweight, obesity, abdominal obesity and obesity-related risk factors in southern China","volume":"12","author":"Hu","year":"2017","journal-title":"PLoS One"},{"key":"ref5","article-title":"Genetics, obesity, and environmental risk factors associated with type 2 diabetes","author":"Bener","year":""},{"key":"ref6","doi-asserted-by":"publisher","first-page":"13","DOI":"10.1038\/nrendo.2012.199","article-title":"Global obesity: trends, risk factors and policy implications","volume":"9","author":"Malik","year":"2013","journal-title":"Nat Rev Endocrinol"},{"key":"ref7","first-page":"271","author":"Amin","year":"2019"},{"key":"ref8","volume-title":"Artificial intelligence with multi-functional machine learning platform development for better healthcare and precision medicine. 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