Joint ensemble learning-based risk prediction of Alzheimer's disease among mild cognitive impairment patients
The 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.
Authors
- Haixia Su
- Chunling Dong (ORCID: https://orcid.org/0000-0003-3968-5556)
- Yuhai Zhang (ORCID: https://orcid.org/0009-0004-5772-7427)
- Zhijun Tan (ORCID: https://orcid.org/0000-0003-4655-6314)
- Peng Yang (ORCID: https://orcid.org/0000-0001-8964-0597)
- Zhongwen Hu (ORCID: https://orcid.org/0000-0003-2689-3196)
- Tianyuan Guan (ORCID: https://orcid.org/0000-0002-5921-3209)
- Xueying Li (ORCID: https://orcid.org/0000-0003-1984-3609)
- Zhaoyu Li (ORCID: https://orcid.org/0000-0002-5222-6832)
- Lei Shang (ORCID: https://orcid.org/0000-0001-8410-5149)
Institutions
- Air Force Engineering University (CN)
Publication Details
- Journal
- The Journal of Prevention of Alzheimer s Disease
- Published
- 2025-02-05
- DOI
- https://doi.org/10.1016/j.tjpad.2025.100083
- Citations
- 4
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 5.88
Funders
- National Natural Science Foundation of China
- National Institutes of Health
- National Institute on Aging