Explainable Machine Learning Reveals Time-Dependent Cognitive Risk in Minor Neurocognitive Disorder: Implications for Health Promotion and Early Risk Stratification
: These findings highlight the potential of explainable ML as a health promotion tool and suggest that explainable ML can uncover clinically meaningful cognitive risk signatures at the earliest stages of NCD. By identifying modifiable systemic contributors alongside cognitive risk profiles, this framework supports precision-oriented preventive strategies and proactive longitudinal monitoring in minor NCD.
Authors
- Κonstantinos Vadikolias (ORCID: https://orcid.org/0000-0003-0484-369X)
- Christos Kokkotis (ORCID: https://orcid.org/0000-0003-2972-3117)
- Dimitrios Tsiptsios (ORCID: https://orcid.org/0000-0002-1601-8788)
- Anna Tsiakiri (ORCID: https://orcid.org/0000-0003-3341-4093)
- Leonidas D Panos (ORCID: https://orcid.org/0000-0002-5372-1819)
- Foteini Christidi (ORCID: https://orcid.org/0000-0003-1297-9415)
- Nikolaos Aggelousis (ORCID: https://orcid.org/0000-0001-5108-7335)
Institutions
- Democritus University of Thrace (GR)
- National and Kapodistrian University of Athens (GR)
- Aristotle University of Thessaloniki (GR)
Publication Details
- Journal
- Biomedicines
- Published
- 2026-04-12
- DOI
- https://doi.org/10.3390/biomedicines14040880
- Primary Topic
- Dementia and Cognitive Impairment Research
- Type
- article
- Field-Weighted Citation Impact
- 0.00