ArticleBMC nursing2026
Which machine learning algorithms are best at predicting eating disorder tendencies among nursing students in China?
Article in BMC nursing, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
backgroundNursing students are at high risk for developing eating disorders (ED). However, methods for identifying ED tendencies within this group remain underexplored. PURPOSE: This study aims to develop and validate machine learning-based models for predicting ED tendencies among nursing students.
methodsA cross-sectional study was conducted with a sample of nursing students in mainland China (N = 1709). Machine learning models were developed using a training set (N = 1196), and internally validated using a test set (N = 513). Model performance was evaluated based on widely used metrics, including the area under the receiver operating characteristic curve (AUC).
resultsThe random forest performed best in test set, achieving a Bootstrapped_AUC of 0.804 (95% CI, 0.787, 0.818), a Bootstrapped_AP of 0.711 (95% CI, 0.682, 0.739), and a brier score of 0.170. The non-black box models showed that rumination related to ED, low body satisfaction, and depression were significant risk factors of eating disorders tendencies among nursing students.
conclusionMachine learning-based models can help nursing educators to identify nursing students at risk of ED. Additionally, addressing cognitive biases regarding body shape and weight, as well as alleviating depressive symptoms, may help reduce ED risk among nursing students. CLINICAL TRIAL NUMBER: Not applicable.
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