Optimizing deep learning models to combat amyotrophic lateral sclerosis (ALS) disease progression
These findings convincingly demonstrate the potential of optimized XGBoost and deep learning for ALS progression prediction and classification, particularly with optimized parameters. This approach offers significant potential for early risk stratification, personalized treatment planning, enhanced prognostic communication, diagnostic support, streamlined disease monitoring, and improved clinical decision-making, ultimately contributing to better patient outcomes and potentially reducing ALS-related mortality.
Authors
- Faisal Mehmood Butt
- Fuad A. A. Awwad (ORCID: https://orcid.org/0000-0001-9541-5695)
- Umair Ahmad Salaria (ORCID: https://orcid.org/0000-0002-2356-1113)
- Xu Yan (ORCID: https://orcid.org/0009-0004-6587-1410)
- Emad A.A. Ismail
- Lal Hussain (ORCID: https://orcid.org/0000-0003-1103-4938)
- Hua‐Li Qin (ORCID: https://orcid.org/0000-0002-6609-0083)
- Ziang Liu (ORCID: https://orcid.org/0009-0004-4214-3648)
Institutions
- University of Azad Jammu and Kashmir (PK)
- Trine University (US)
- King Saud University (SA)
- University of Business and Technology (SA)
- Carnegie Mellon University (US)
Publication Details
- Journal
- Digital Health
- Published
- 2025-05-01
- DOI
- https://doi.org/10.1177/20552076251349719
- Citations
- 5
- Primary Topic
- Amyotrophic Lateral Sclerosis Research
- Type
- article
- Field-Weighted Citation Impact
- 6.95