A Multivariable Prediction Model for Mild Cognitive Impairment and Dementia: Algorithm Development and Validation
ML algorithms, especially XGBoost, exhibited the potential for predicting MCI onset using KLoSA data. However, no model has demonstrated robust accuracy in predicting MCI and dementia. Sociodemographic and health-related factors are crucial for initiating cognitive conditions, emphasizing the need for multifaceted predictive models for early identification and intervention. These findings underscore the potential and limitations of ML in predicting cognitive impairment in community-dwelling older adults.
Authors
- Sarah Soyeon Oh (ORCID: https://orcid.org/0000-0001-5709-2311)
- Bada Kang (ORCID: https://orcid.org/0000-0002-7002-2315)
- Minkyu Jeon (ORCID: https://orcid.org/0000-0003-0572-6065)
- Jennifer Ivy Kim (ORCID: https://orcid.org/0000-0003-1845-2200)
- Jinyeop Song (ORCID: https://orcid.org/0000-0002-4113-5185)
- Dahye Hong (ORCID: https://orcid.org/0009-0002-6054-773X)
- Hyewon Jeong (ORCID: https://orcid.org/0000-0002-6552-5276)
Institutions
- Yonsei University (KR)
- Princeton University (US)
- Empowerment Program (US)
- Massachusetts Institute of Technology (US)
Publication Details
- Journal
- JMIR Medical Informatics
- Published
- 2024-11-22
- DOI
- https://doi.org/10.2196/59396
- Citations
- 4
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 1.54