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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
Open accessAbstract
Alzheimer’s disease (AD) is the most common type of dementia. Its diagnosis and progression detection have been intensively studied. Nevertheless, research studies often have little effect on clinical practice mainly due to the following reasons: (1) Most studies depend mainly on a single modality, especially neuroimaging; (2) diagnosis and progression detection are usually studied separately as two independent problems; and (3) current studies concentrate mainly on optimizing the performance of complex machine learning models, while disregarding their explainability. As a result, physicians struggle to interpret these models, and feel it is hard to trust them. In this paper, we carefully develop an accurate and interpretable AD diagnosis and progression detection model. This model provides physicians with accurate decisions along with a set of explanations for every decision. Specifically, the model integrates 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) real-world dataset: 294 cognitively normal, 254 stable mild cognitive impairment (MCI), 232 progressive MCI, and 268 AD. It is actually a two-layer model with random forest (RF) as classifier algorithm. In the first layer, the model carries out a multi-class classification for the early diagnosis of AD patients. In the second layer, the model applies binary classification to detect possible MCI-to-AD progression within three years from a baseline diagnosis. The performance of the model is optimized with key markers selected from a large set of biological and clinical measures. Regarding explainability, we provide, for each layer, global and instance-based explanations of the RF classifier by using the SHapley Additive exPlanations (SHAP) feature attribution framework. In addition, we implement 22 explainers based on decision trees and fuzzy rule-based systems to provide complementary justifications for every RF decision in each layer. Furthermore, these explanations are represented in natural language form to help physicians understand the predictions. The designed model achieves a cross-validation accuracy of 93.95% and an F1-score of 93.94% in the first layer, while it achieves a cross-validation accuracy of 87.08% and an F1-Score of 87.09% in the second layer. The resulting system is not only accurate, but also trustworthy, accountable, and medically applicable, thanks to the provided explanations which are broadly consistent with each other and with the AD medical literature. The proposed system can help to enhance the clinical understanding of AD diagnosis and progression processes by providing detailed insights into the effect of different modalities on the disease risk.
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“El-Sappagh et al. performed early and late fusion to create an interpretable Alzheimer’s diagnosis and progression detection model158. Their best performing model was one that implemented instance-based explanations of the random forest classifier by using the SHapley Additive exPlanations (SHAP) feature attribution. Despite using clinical, genomic, and imaging data, the most influential feature”
From Multimodal machine learning in precision health: A scoping review (2022) · cites this paper as [158]
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Metadata
- Authors
- El-Sappagh, S., Alonso, J. M., Islam, S. M. R., Sultan, A. M., & Kwak, K. S.
- Affiliations
- Sejong University (KR)
- Inha University (KR)
- Corresponding author
- Kwak, Kyung Sup
- Venue
- Scientific Reports
- Published
- 2021-01-29
- Volume
- 11
- Issue
- 1
- Pages
- 2660-2660
- ISSN
- 2045-2322 (ISSN-L)
- PMC
- PMC7846613
- OpenAlex
- W3125069671
- Cited by
- 363
- References
- 108
- Citations by year
- 2026: 62 · 2025: 96 · 2024: 80 · 2023: 58 · 2022: 56 · 2021: 11
- FWCI
- 36.77
- Type
- journal article
- Language
- en
- Open access
- gold
- doi.org
- Locations
- Scientific Reports (publishedVersion, cc-by)
- PubMed Central (publishedVersion, cc-by)
- PubMed (publishedVersion)
- Aberdeen University Research Archive (Aberdeen University) (submittedVersion)
- LA Referencia (Red Federada de Repositorios Institucionales de Publicaciones Científicas) (submittedVersion, cc-by)
- DOAJ (DOAJ: Directory of Open Access Journals) (submittedVersion, cc-by-sa)
- Hispana (submittedVersion, cc-by)
- Repositorio institucional da Universidade de Santiago de Compostela (University of Santiago de Compostela) (submittedVersion, other-oa)
- Greater South Information System
- Funders
- National Research Foundation of Korea-Grant funded by the Korean Government (NRF-2017R1A2B2012337)
- Topics
- Health Sciences › Medicine › Psychiatry and Mental health › Dementia and Cognitive Impairment Research
- Health Sciences › Health Professions › Health Information Management › Artificial Intelligence in Healthcare
- Physical Sciences › Computer Science › Artificial Intelligence › Explainable Artificial Intelligence (XAI)
- Keywords
- Alzheimer's disease, Alzheimer's disease diagnosis, multimodal fusion, explainable AI, SHAP feature attribution, random forest, fuzzy rule-based systems, natural language explanations, clinical decision support, Alzheimer's Disease Neuroimaging Initiative, biomarker selection, model interpretability
- MeSH terms
- Aged, Aged, 80 and over, Alzheimer Disease, Artificial Intelligence, Brain, Female, Follow-Up Studies, Humans, Magnetic Resonance Imaging, Male, Middle Aged, Models, Neurological, Databases, Factual, Neuroimaging
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
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