What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants
BackgroundObesity 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. Th...
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Frontiers Media S.A.
2025-07-01
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| Series: | Frontiers in Nutrition |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnut.2025.1574063/full |
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| author | Radwan Qasrawi Radwan Qasrawi Abir Ajab Leila Cheikh Ismail Leila Cheikh Ismail Ayesha Al Dhaheri Sharifa Alblooshi Razan Abu Ghoush Stephanny Vicuna Polo Malak Amro Suliman Thwib Ghada Issa Haleama Al Sabbah |
| author_facet | Radwan Qasrawi Radwan Qasrawi Abir Ajab Leila Cheikh Ismail Leila Cheikh Ismail Ayesha Al Dhaheri Sharifa Alblooshi Razan Abu Ghoush Stephanny Vicuna Polo Malak Amro Suliman Thwib Ghada Issa Haleama Al Sabbah |
| author_sort | Radwan Qasrawi |
| collection | DOAJ |
| description | BackgroundObesity 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.MethodsThis 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 < 0.05, 95% CI).ResultsThe Random Forest model achieved the highest accuracy (obesity: 96.8%, underweight: 94.6%, overweight: 90.3%) and AUC (0.95–0.97). 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 (p < 0.001). Socio-demographic factors (e.g., low income and marital status) and lifestyle habits (e.g., sleep <5 h and fast eating) were also significantly related to weight status.ConclusionThe 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. |
| format | Article |
| id | doaj-art-c482beddfc294fc599c4b7d7431f4775 |
| institution | Kabale University |
| issn | 2296-861X |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Frontiers Media S.A. |
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| series | Frontiers in Nutrition |
| spelling | doaj-art-c482beddfc294fc599c4b7d7431f47752025-08-20T03:51:09ZengFrontiers Media S.A.Frontiers in Nutrition2296-861X2025-07-011210.3389/fnut.2025.15740631574063What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinantsRadwan Qasrawi0Radwan Qasrawi1Abir Ajab2Leila Cheikh Ismail3Leila Cheikh Ismail4Ayesha Al Dhaheri5Sharifa Alblooshi6Razan Abu Ghoush7Stephanny Vicuna Polo8Malak Amro9Suliman Thwib10Ghada Issa11Haleama Al Sabbah12Department of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Computer Engineering, Istinye University, Istanbul, TürkiyeDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab EmiratesDepartment of Clinical Nutrition and Dietetics, College of Health Sciences, University of Sharjah, Sharjah, United Arab EmiratesNuffield Department of Women’s & Reproductive Health, University of Oxford, Oxford, United KingdomDepartment of Nutrition and Health, College of Medicine and Health Sciences, UAE University, Al Ain, United Arab EmiratesDepartment of Health Sciences, College of Natural and Health Sciences, Zayed University, Dubai, United Arab EmiratesDepartment of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Computer Science, Al-Quds University, Jerusalem, PalestineDepartment of Public Health, College of Health Sciences, Abu Dhabi University, Abu Dhabi, United Arab EmiratesBackgroundObesity 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.MethodsThis 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 < 0.05, 95% CI).ResultsThe Random Forest model achieved the highest accuracy (obesity: 96.8%, underweight: 94.6%, overweight: 90.3%) and AUC (0.95–0.97). 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 (p < 0.001). Socio-demographic factors (e.g., low income and marital status) and lifestyle habits (e.g., sleep <5 h and fast eating) were also significantly related to weight status.ConclusionThe 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.https://www.frontiersin.org/articles/10.3389/fnut.2025.1574063/fullbody mass indexdietary patternslifestyle behaviorsmachine learningobesityweight management |
| spellingShingle | Radwan Qasrawi Radwan Qasrawi Abir Ajab Leila Cheikh Ismail Leila Cheikh Ismail Ayesha Al Dhaheri Sharifa Alblooshi Razan Abu Ghoush Stephanny Vicuna Polo Malak Amro Suliman Thwib Ghada Issa Haleama Al Sabbah What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants Frontiers in Nutrition body mass index dietary patterns lifestyle behaviors machine learning obesity weight management |
| title | What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants |
| title_full | What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants |
| title_fullStr | What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants |
| title_full_unstemmed | What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants |
| title_short | What drives weight status among female university students? A machine learning analysis of sociodemographic, dietary, and lifestyle determinants |
| title_sort | what drives weight status among female university students a machine learning analysis of sociodemographic dietary and lifestyle determinants |
| topic | body mass index dietary patterns lifestyle behaviors machine learning obesity weight management |
| url | https://www.frontiersin.org/articles/10.3389/fnut.2025.1574063/full |
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