Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.

ABSTRACT:

Objective

Due to the recognition for the importance of early intervention in Alzheimer's disease (AD), it is important to focus on prevention and treatment strategies for mild cognitive impairment (MCI). This study aimed to establish a risk prediction model for AD among MCI patients to provide clinical guidance for primary medical institutions.

Methods

Data from MCI subjects were obtained from the NACC. Importance ranking and the SHapley Additive exPlanations (SHAP) method for the Random Survival Forest (RSF) and Extreme Gradient Boosting (XGBoost) algorithms in ensemble learning were adopted to select the predictors, and hierarchical clustering analysis was used to mitigate multicollinearity. The RSF, XGBoost and Cox proportional hazard regression (Cox) models were establis

SUBMITTER: Guan T 

PROVIDER: S-EPMC12184056 | biostudies-literature | 2025 Apr

REPOSITORIES: biostudies-literature