Explainable machine learning models for early Alzheimer’s disease detection using multimodal clinical data
The integration of explainable AI techniques with machine learning models provides clinically meaningful insights for AD prediction, enhancing transparency and fostering trust in AI-driven diagnostic tools whilst maintaining high predictive accuracy. Future work should focus on external validation, clinical workflow integration, and addressing computational requirements for real-world deployment.
Authors
- David B. Olawade (ORCID: https://orcid.org/0000-0003-0188-9836)
- Nicholas Aderinto (ORCID: https://orcid.org/0000-0003-0004-7389)
- Afeez A Soladoye (ORCID: https://orcid.org/0000-0002-6349-5173)
- Damilola Osho
Institutions
- University of East London (GB)
- South Essex Partnership University Foundation NHS Trust (GB)
- Federal University Oye Ekiti (NG)
- Ladoke Akintola University of Technology (NG)
Publication Details
- Journal
- International Journal of Medical Informatics
- Published
- 2025-08-27
- DOI
- https://doi.org/10.1016/j.ijmedinf.2025.106093
- Citations
- 10
- Primary Topic
- Machine Learning in Healthcare
- Type
- article
- Field-Weighted Citation Impact
- 24.81