An explainable machine learning approach for Alzheimer’s disease classification
Imam Sadiq University · University of Information Technology and Communications · +3 more institutions
Abstract
The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine…
Citation impact
- FWCI
- 43.85
- Percentile
- 100%
- References
- 55
Authors
5- ASAbbas Saad AlatranyCorresponding
Imam Sadiq University, University of Information Technology and Communications, NIHR Leicester Biomedical Research Centre, Liverpool John Moores University
- WKWasiq Khan
Liverpool John Moores University
- AHAbir Hussain
University of Sharjah, Liverpool John Moores University
- HKHoshang Kolivand
Liverpool John Moores University
- DADhiya Al‐Jumeily
Liverpool John Moores University
Topics & keywords
- Artificial intelligence
- Support vector machine
- Computer science
- Machine learning
- Multiclass classification
- Binary classification
- Task (project management)
- Set (abstract data type)