ArticleThe journal of prevention of Alzheimer's disease2025
Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients.
Article in The journal of prevention of Alzheimer's disease, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- Explainable Patient-Level Cognitive Impairment Screening via Temporal, Semantic, and Psycholinguistic Multimodal AI.Journal of Intelligence · 2026Article
- The role of ionotropic glutamate receptors in Alzheimer's disease: A scientometric analysis.Journal of Alzheimer's disease : JAD · 2026Article
- Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.Scientific reports · 2026Article
- AI-Based Classification of Mild Cognitive Impairment and Cognitively Normal Patients.Journal of clinical medicine · 2025Article
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10 authors.
Funding
Abstract
objectiveDue 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.
methodsData 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 established to predict the risk of AD among MCI patients. Additionally, the effects of the three models were evaluated.
resultsA total of 3674 subjects with MCI were included. Thirteen predictors were ultimately identified. In the validation set, the concordance indices were 0.781 (RSF), 0.781 (XGBoost), and 0.798 (Cox), and the Integrated Brier Score was 0.087 (Cox). The prediction effects of the XGBoost and RSF models were not better than those of the Cox model.
conclusionThe ensemble learning method can effectively select predictors of AD risk among MCI subjects. The Cox proportional hazards regression model could be used in primary medical institutions to rapidly screen for the risk of AD among MCI patients once the model is fully clinically validated. The predictors were easy to explain and obtain, and the prediction of AD was accurate.
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