Multimodal machine learning for early risk stratification of post-stroke cognitive impairment

Abstract

Background

Post-stroke cognitive impairment (PSCI) is a major vascular contributor to dementia, significantly impacting long-term recovery and quality of life. Developing accurate prediction models are essential for early identification and timely intervention in high-risk individuals.

Objective

To develop and validate a stacking-based multimodal machine learning model integrating clinical, demographic, and neuroimaging features for early PSCI prediction in acute ischemic stroke (AIS) patients.

Methods

In this retrospective cohort study, 1070 AIS patients admitted to Lianyungang First People's Hospital from January 2020 to August 2023 were included. Demographic, clinical, and neuroimaging data were collected, and cognitive function was assessed 3–6 months post-stroke. PSCI was defined as a z-score ≤ -2.0 in at least one of four cognitive domains. A stacking ensemble model was developed, combining six base algorithms: XGBoost, Gradient Boosting Decision Trees, CatBoost, Support Vector Machine, Logistic Regression, and LightGBM. The final prediction was generated by a meta-model trained on base model outputs.

Results

Of the 1070 patients (mean age 67.4 ± 9.3 years, 61.5% male), 37.2% developed PSCI. The stacking model achieved 98.13% accuracy, 0.9972 AUC, and 0.9744 F1-score in internal validation. External validation showed 81.00% accuracy, 0.9049 AUC, and 0.8780 recall. Key predictors of PSCI included infarct volume, cortical lesions, medial temporal lobe atrophy, and baseline NIHSS score.

Conclusions

This stacking-based multimodal machine learning model demonstrates robust predictive performance for PSCI risk in AIS patients, serving as a reliable tool for early detection that may inform personalized intervention strategies to prevent progression to post-stroke dementia.