Machine learning algorithms and traditional statistical models for detection of dementia: a population-based study
We developed and evaluated the performance of LR, RF and XGBoost models for dementia detection in a rural older population in China. All the three models showed excellent discrimination and calibration. The XGBoost model exhibited the best performance and clinical utility in dementia detection. These models are based on information readily available in routine clinical practice, which could be easily implemented amongst rural older populations for dementia detection.
Authors
- Jiaqi Dong (ORCID: https://orcid.org/0000-0002-9505-8656)
- Na Tian (ORCID: https://orcid.org/0000-0001-5627-5095)
- Lin Song (ORCID: https://orcid.org/0000-0002-1245-681X)
- Chengxuan Qiu (ORCID: https://orcid.org/0000-0003-1922-4912)
- Min Zhu (ORCID: https://orcid.org/0000-0002-7468-0083)
- Yi Dong (ORCID: https://orcid.org/0000-0001-6630-112X)
- Lin Cong
- Guoqing Zhang (ORCID: https://orcid.org/0000-0003-2169-9060)
- Yongxiang Wang (ORCID: https://orcid.org/0000-0002-2003-6006)
- Yuqi Li (ORCID: https://orcid.org/0000-0002-7985-7909)
- Cuicui Liu (ORCID: https://orcid.org/0000-0003-1592-9384)
- Keke Liu (ORCID: https://orcid.org/0000-0003-2222-5940)
- Jiafeng Wang (ORCID: https://orcid.org/0009-0005-3990-4888)
- Shi Tang
- Tingting Hou
- Yifeng Du
Institutions
- Stockholm County Council (SE)
- Karolinska Institutet (SE)
- Shandong Provincial Hospital (CN)
- Shandong First Medical University (CN)
Publication Details
- Journal
- Age and Ageing
- Published
- 2025-03-30
- DOI
- https://doi.org/10.1093/ageing/afaf085
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00