September 10, 2025Frontiers in NutritionOpen Access

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

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Authors

RQRadwan Qasrawi AAAbir Ajab LILeila Cheikh IsmailUniversity of Sharjah

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Implication

Cross-sectional analysis identifies dietary, lifestyle, and sociodemographic factors affecting weight status in university females, suggesting targeted interventions.

Key Points

Weight status among female university students is influenced by various dietary and lifestyle factors.
The Random Forest model achieved the highest accuracy in predicting obesity at 96.8% and underweight at 94.6%.
Machine learning techniques, including Random Forest and SVM, were utilized to analyze data from 7,092 participants.
Findings may inform public health strategies, highlighting modifiable factors associated with weight status.

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Qasrawi et al. (2025) studied this question.

synapsesocial.com/papers/68c24126b210217d6479e3fa https://doi.org/10.3389/fnut.2025.1574063

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Implication

Cross-sectional analysis identifies dietary, lifestyle, and sociodemographic factors affecting weight status in university females, suggesting targeted interventions.

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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

Discussion

Member takes

Implication

Key Points

Cite This Study

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