Objective  To explore the influencing factors of mild cognitive impairment (MCI) in hospitalized elderly patients with multiple comorbidities, and to construct a MCI risk prediction model based on machine learning (ML) methods.

Methods  The study included elderly patients with multiple comorbidities admitted to the First Affiliated Hospital of Xinjiang Medical University as research subjects. Single factor analysis and least absolute shrinkage and selection operator regression algorithms were used to screen for MCI risk factors. Nine different ML methods were used, including random forest, light gradient boosting machine, extreme gradient boosting, Logistic regression, K-nearest neighbor classification algorithm, support vector machine, artificial neural network, decision tree, and elastic network regression algorithm, to construct MCI risk prediction models. Shapley addition explanation (SHAP) algorithm was used to explain the final model.

Results  A total of 920 hospitalized elderly patients with multiple comorbidities were included, including 261 cases in the MCI group. The random forest model had the best predictive performance, with a higher area under the receiver operating characteristic curve than other models. The SHAP algorithm identified the age, comorbidities, education level, and cerebrovascular disease in the random forest model as key decision factors for predicting MCI in hospitalized elderly patients with multiple comorbidities. The calibration curve showed that the predictive performance of the model was basically consistent with the actual results, and the decision curve indicated that the model had good clinical applicability.

Conclusion  Advanced age, increased comorbidities, and cerebrovascular disease are risk factors for MCI in hospitalized elderly people with multiple comorbidities. High educational level is a protective factor for MCI in hospitalized elderly people with multiple comorbidities. Based on machine learning algorithms, the prediction model for MCI risk using random forest has the best predictive performance and good clinical applicability, which can assist in cognitive management and more accurate medical intervention for more efficient elderly comprehensive assessment in clinical practice.

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