ORIGINAL RESEARCH article
Front. Neurosci.
Sec. Brain Imaging Methods
This article is part of the Research TopicMachine Learning-Driven Insights into Cognitive Aging and Behavioral ChangesView all 6 articles
A Comparative Study of Risk Factors in Predictive Models for Cognitive Dysfunction in Patients with Leukoaraiosis Based on Machine Learning Algorithms
Provisionally accepted
- Department of Neurology, Qixia Branch of Jiangsu Provincial Hospital, Nanjing, China
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Objective: To explore the risk factors of cognitive dysfunction in patients with leukoaraiosis(LA) and to construct a predictive model using machine learning. Methods: A total of 273 patients with LA were included. Univariate analysis and multivariate logistic regression were performed to identify independent risk factors for cognitive dysfunction. The patients were divided into a training set (191 cases) and a validation set (82 cases) in a 7:3 ratio. Seven machine learning algorithms (Decision Tree, GBDT, Logistic Regression, Random Forest, SVM, KNN, XGBoost) were employed to construct predictive models. Evaluation metrics included accuracy, recall, F1 score, MCC, AUROC, and SHAP was used for model interpretation. Results: Univariate analysis revealed that age, LDL-C, uric acid, CRP, Fazekas score, and IASA score were associated with cognitive dysfunction (p<0.05). Multivariate logistic regression analysis showed that age, LDL-C, uric acid, Fazekas score, and IASA score were independent risk factors (p<0.05). Among the machine learning algorithms, the Random Forest model performed the best, with an AUROC of 0.8373 for the validation set. SHAP analysis indicated that age, LDL-C, IASA score, and Fazekas score were the most important predictors. Conclusion: The Random Forest model can be used to predict the risk of cognitive dysfunction in patients with LA, providing a reference for early warning.
Keywords: Leukoaraiosis, cognitive dysfunction, machine learning, predictive model, Risk factors
Received: 27 Jun 2025; Accepted: 15 Oct 2025.
Copyright: © 2025 Xu, Zhang, Huang, Lu, Yin and Li. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence: Guoxin Zhang, zhangguoxin.njqxyy@foxmail.com
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