Construction and evaluation of a machine learning-based predictive model for enteral nutrition feeding intolerance risk in ICU patients.

Objective: We aim to investigate the factors influencing enteral nutrition feeding intolerance (ENFI) in critically ill patients and develop a risk prediction model for ENFI in intensive care unit (ICU) patients, utilizing three machine learning algorithms. This model will serve as an assessment tool for preventing and managing ENFI in ICU patients. Methods: A total of 487 ICU patients from a tertiary hospital in Zhejiang Province between January 2021 and December 2023 were selected as the study subjects. The patients were randomly divided into a training set and a test set in an 8:2 ratio. Three machine learning algorithms-logistic regression (LR), support vector machine (SVM), and random forest (RF)-were used to construct the risk prediction model for ENFI in ICU patients. The predictive performance of the three models was compared using metrics such as AUC (area under the ROC curve), accuracy, precision, recall, and F1 score. Results: The logistic regression model achieved an AUC of 0.9308, with an accuracy of 94.3%, precision of 95.4%, recall of 88.6%, and an F1-score of 0.9185 in correctly identifying ENFI risk in ICU patients. The random forest model attained an AUC of 0.9511, with an accuracy of 96.1%, precision of 97.7%, recall of 91.4%, and an F1-score of 0.9446. The support vector machine (SVM) model yielded an AUC of 0.9241, with an accuracy of 94.1%, precision of 96.8%, recall of 86.4%, and an F1-score of 0.9132. Conclusion: The random forest model performed the best in this study, demonstrating superior predictive performance.