Cited by 363Dementia and Cognitive Impairment ResearchArtificial Intelligence in HealthcareExplainable Artificial Intelligence (XAI)

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

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El-Sappagh, Shaker, Alonso, Jose M., Islam, S. M. Riazul, Sultan, Ahmad M., Kwak, Kyung Sup·2021·Scientific Reports

DOI 10.1038/s41598-021-82098-3PMC PMC7846613

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Abstract

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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Figure 1 Selected features for both layers based on three different techniques of SVM, RF, and GB. The first row is for the first layer, and the second row is for the second layer.CC BY · PubMed Central

Figure 2 SHAP summary plots for the first layer. The upper left figure represents the CN class, the upper right figure represents the MCI class, and the second row represents the AD class.CC BY · PubMed Central

Figure 3 SHAP summary plots for the second layer. The left figure shows the pMCI class, and the right figure shows the sMCI class.CC BY · PubMed Central

Figure 4 First layer example predictions for AD (A), CN (B), and MCI (C) and SHAP supervised clustering in model behavior for all cases in each class. Red indicates attributions that push the score higher, while blue indicates contributions that push the score lower. A few of the noticeable subgroups are annotated with the features that define them.CC BY · PubMed Central

Figure 5 Development process for the oracle model in each layer.CC BY · PubMed Central

Figure 6 The proposed XAI framework. A variety of data modalities are used to build the predictive model. In addition, a variety of explanations are built for the entire RF behavior and for each prediction. The FreeSurfer version 6.0 is used (https://surfer.nmr.mgh.harvard.edu/).CC BY · PubMed Central

Figure 7 Multimodal fusion strategies: (a) late fusion, (b) early fusion.CC BY · PubMed Central

Figure 8 Roles of explainers to enhance RF interpretability.CC BY · PubMed Central

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Random Forests
Breiman, L. (2001) · Machine Learning · doi:10.1023/a:1010933404324 · Cited by 133,538
SMOTE synthetic minority over-sampling technique
Chawla, N. V., Bowyer, K. W., Hall, L. O., & Kegelmeyer, W. P. (2002) · Journal of Artificial Intelligence Research · doi:10.1613/jair.953 · Cited by 32,865
Clinical diagnosis of Alzheimer's disease: Report of the NINCDS‐ADRDA Work Group* under the auspices of Department of Health and Human Services Task Force on Alzheimer's Disease
McKhann, G., Drachman, D., Folstein, M., Katzman, R., Price, D., & Stadlan, E. M. (1984) · Neurology · doi:10.1212/wnl.34.7.939 · Cited by 27,915
A Unified Approach to Interpreting Model Predictions
Lundberg, S., & Lee, S.-I. (2017) · arXiv · doi:10.48550/arxiv.1705.07874 · Cited by 23,707 · cited 2×
“SHAP is based on the Shapely value concept from game theory 91,92 .”
Method
Untitled work
(1992) · Cited by 23,431
Advances in neural information processing systems 7
Cios, K. J., & Shields, M. E. (1997) · Neurocomputing · doi:10.1016/s0925-2312(97)90018-7 · Cited by 22,093
"Why Should I Trust You?": Explaining the Predictions of Any Classifier
Ribeiro, M. T., Singh, S., & Guestrin, C. (2016) · Proceedings of the 22nd ACM SIGKDD International Conference on Knowledge Discovery and Data Mining · doi:10.1145/2939672.2939778 · Cited by 17,555 · cited 3×
“Fernandez-Delgado et al. 40 evaluated 179 classifiers using different UCI datasets, and concluded that RF outperforms other classifiers, including SVMs and neural networks.”
BackgroundMethod
Ror2 signaling regulates Golgi structure and transport through IFT20 for tumor invasiveness
Nishita, M., Park, S.-Y., Nishio, T., Kamizaki, K., Wang, Z., Tamada, K., Takumi, T., Hashimoto, R., Otani, H., Pazour, G. J., et al. (2017) · Scientific Reports · doi:10.1038/s41598-016-0028-x · Cited by 15,947
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest
Desikan, R. S., Ségonne, F., Fischl, B., Quinn, B. T., Dickerson, B. C., Blacker, D., Buckner, R. L., Dale, A. M., Maguire, R. P., Hyman, B. T., et al. (2006) · NeuroImage · doi:10.1016/j.neuroimage.2006.01.021 · Cited by 13,324
A Study of Cross-Validation and Bootstrap for Accuracy Estimation and Model Selection
Kohavi, R. (1995) · Zenodo · doi:10.5281/zenodo.19712698 · Cited by 10,909

Show all 108 references

Research works printed but not linked: 2

Fernandez-Delgado, M., Cernadas, E., Barro, S. & Amorim, D. Do we need hundreds of classifiers to solve real world classification problems. J. Mach. Learn. Res. 15, 3133–3181 (2014).
Elisseeff, A. & Pontil, M. Leave-one-out error and stability of learning algorithms with applications. In NATO science series sub series iii computer and systems sciences 111–130 (ISO Press, 2003).

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BackgroundMethod
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Background
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“El-Sappagh et al. [20] introduced a two-layer ML model for the diagnosis and tracking of AD progression.”
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“[19] developed an EAI detection and prediction model for Alzheimer’s disease.”
Background
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Exploration and analysis of risk factors for coronary artery disease with type 2 diabetes based on SHAP explainable machine learning algorithm
Tang, D., Liang, F., Gu, X., Jin, Y., Hu, X., Fen, L., & Yang, Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-11142-3 · Cited by 9
“Its capabilities include quantifying the overall contribution of each feature, illustrating their specific influence on individual predictions, examining feature interactions, and analyzing the combined effects of feature dependencies 26 .”
Predicting the Geopolymerization Process of Fly-Ash-Based Geopolymer Using Machine Learning
Chen, K., Cheng, Y., Yu, M., Liu, L., Wang, Y., & Zhang, J. (2022) · Buildings · doi:10.3390/buildings12111792 · Cited by 9
“The difference between the f(x) and the base value represents the feature contribution [39].”
Background
An Interpretable PyCaret Approach for Alzheimer's Disease Prediction
P., A., & GUNASUNDARI, R. (2024) · International Journal of Computational and Experimental Science and Engineering · doi:10.22399/ijcesen.655 · Cited by 11
Layer-Wise Relevance Propagation for Smart-Grid Stability Prediction
Erdem, T., & Eken, S. (2022) · Communications in computer and information science · doi:10.1007/978-3-031-04112-9_24 · Cited by 9
Hierarchical Two-Stage Cost-Sensitive Clinical Decision Support System for Screening Prodromal Alzheimer’s Disease and Related Dementias
Kleiman, M. J., Ariko, T., Galvin, J. E., & Initiative, T. A. D. N. (2022) · Journal of Alzheimer s Disease · doi:10.3233/jad-220891 · Cited by 8
“Previous studies have examined multi-layered prediction algorithms, either by ﬁrst focusing on binary then ternary classiﬁcation [39] or by ﬁrst screening then determining progression risk [40]; however, to our knowledge none have approached multi-stage screening through a cost-sensitive lens.”
Human-Centered Explainable AI at the Edge for eHealth
Dutta, J., & Puthal, D. (2023) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge60047.2023.00044 · Cited by 9
The Use of Artificial Intelligence in the Management of Neurodegenerative Disorders; Focus on Alzheimer's Disease
Khastehband, S., Dabbaghi, K. G., Khosravirad, Z., Jamalnia, S., GhorbaniNia, R., Mahmoudikohani, F., & Zakeri, H. (2023) · Galen Medical Journal · doi:10.31661/gmj.v12i.3061 · Cited by 9
Combining Nonlinear Features of EEG and MRI to Diagnose Alzheimer’s Disease
Rad, E. M., Azarnoosh, M., Ghoshuni, M., & Khalilzadeh, M. (2024) · Annals of Data Science · doi:10.1007/s40745-024-00533-4 · Cited by 8
“Deep learning methods and machine learning techniques can be two effective and accurate for methods in the early diagnosis of Alzheimer’s disease [5].”
Interpretable regional meteorological feature extraction enhances deep learning for extended 120-h PM2.5 forecasting
Xiaolin, L. I. U., Pu, X., Lu, C., Zhang, H., Li, T., Grieneisen, M. L., Li, J., Ma, N., Yan, C., Zhan, Y., et al. (2024) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2024.144287 · Cited by 10
Alzheimer’s disease prediction using CAdam optimized reinforcement learning-based deep convolutional neural network model
Chaudhari, P. A., & Khot, S. S. (2025) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2025.107968 · Cited by 8
A Maturity Model for Practical Explainability in Artificial Intelligence‐Based Applications: Integrating Analysis and Evaluation (MM4XAI‐AE) Models
Muñoz-Ordóñez, J. F., Cobos, C., Vidal-Rojas, J.-C., & Herrera, F. (2025) · International Journal of Intelligent Systems · doi:10.1155/int/4934696 · Cited by 12
IoMT driven Alzheimer’s prediction model empowered with transfer learning and explainable AI approach in healthcare 5.0
Khan, A. H., Ali, D., Ahmed, S., Alhumam, A., Khan, M. F., & Siddiqui, S. Y. (2025) · Scientific Reports · doi:10.1038/s41598-025-19395-8 · Cited by 8
Multimodal AI for Alzheimer Disease Diagnosis: Systematic Review of Datasets, Models, and Modalities
Yu, Z., Mulholland, A. J., Huang, T., & Liu, Q. (2026) · Journal of Medical Internet Research · doi:10.2196/85414 · Cited by 12
Early classification of Alzheimer's disease phenotype based on hippocampal electrophysiology in the TgF344-AD rat model
Moradi, F., van den Berg, M., Mirjebreili, S. M., Kosten, L., Verhoye, M., Amiri, M., & Keliris, G. A. (2023) · iScience · doi:10.1016/j.isci.2023.107454 · Cited by 9
Cross‐Dataset Evaluation of Dementia Longitudinal Progression Prediction Models
Zhang, C., An, L., Wulan, N., Nguyen, K., Orbán, C., Chen, P., Chen, C., Zhou, J. H., Liu, K., Yeo, B. T. T., et al. (2025) · Human Brain Mapping · doi:10.1002/hbm.70280 · Cited by 9
“For example, a typical mild cognitive impairment (MCI) progression task is to predict whether an MCI patient will develop dementia within 3 years (Basaia et al. 2019; El-Sappagh et al. 2021; Ocasio and Duong 2021).”
Risk factors and machine learning model for predicting hospitalization outcomes in geriatric patients with dementia
Wang, X., Ezeana, C. F., Wang, L., Puppala, M., Huang, Y., He, Y., Yu, X., Yin, Z., Zhao, H., Lai, E. C., et al. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12351 · Cited by 7
c-Diadem: a constrained dual-input deep learning model to identify novel biomarkers in Alzheimer’s disease
Jemimah, S., Shehhi, A. A., & Initiative, F. T. A. D. N. (2023) · BMC Medical Genomics · doi:10.1186/s12920-023-01675-9 · Cited by 8
“Moreover, several studies [15–18] have employed SHapley Additive exPlana-tions (SHAP) [19], to understand model prediction and derive insights into disease markers and pathology.”
Result
Improving clinical efficiency in screening for cognitive impairment due to Alzheimer's
Ren, Y., Shahbaba, B., & Stark, C. E. L. (2023) · Alzheimer s & Dementia Diagnosis Assessment & Disease Monitoring · doi:10.1002/dad2.12494 · Cited by 8
Explainable AI-based Alzheimer’s Prediction and Management Using Multimodal DataPreprint
Jahan, S., Taher, K. A., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S. M. S., & Ra, I.-H. (2022) · Preprints.org · doi:10.20944/preprints202203.0214.v1 · Cited by 8
Artificial Intelligence Powered Biomarker Discovery: A Large-Scale Analysis of 236 Studies Across 19 Therapeutic Areas and 147 DiseasesPreprint
Abbasi, A. F., Naveed, S., Asim, M. N., Sajjad, M., Vollmer, S. J., & Dengel, A. (2025) · bioRxiv (Cold Spring Harbor Laboratory) · doi:10.1101/2025.08.28.672795 · Cited by 8
“In terms of ML and DL models, our review identifies the widespread use of classical ML algorithms namely, SVM [150, 191, 210, 221, 279, 294], RF [127, 137, 164, 208, 235, 236, 299, 306], LR [120, 123, 125, 221, 294] alongside an increasing adoption of NN-based models [122, 141, 145, 148, 189 ?”
The disagreement dilemma in explainable AI: can bias reduction bridge the gap
Bhardwaj, N., & Parashar, G. (2025) · International Journal of Systems Assurance Engineering and Management · doi:10.1007/s13198-025-02712-9 · Cited by 7
Exploring the gut-brain-microbiome axis in Alzheimer's disease: Integrating metagenomics, metabolomics, and artificial intelligence for next-generation biomarker discovery
Oso, T. A., Ahmed, M. M., Okesanya, O. J., Adebayo, U. O., Obadeyi, K. B., Othman, Z. K., & Lucero‐Prisno, D. E. (2025) · Journal of Alzheimer s Disease · doi:10.1177/13872877251407700 · Cited by 9
Explainable AI in Disease Diagnosis
Bedi, P., Thukral, A., & Dhiman, S. (2024) · Computational intelligence methods and applications · doi:10.1007/978-981-97-3705-5_5 · Cited by 7
HiMAL: Multimodal Hi erarchical M ulti-task A uxiliary L earning framework for predicting Alzheimer’s disease progression
Kumar, S., Yu, S., Michelson, A. P., Kannampallil, T., & Payne, P. (2024) · JAMIA Open · doi:10.1093/jamiaopen/ooae087 · Cited by 10
“49,58,59 Recent research on AD progression only identified the important features for a prediction, providing a snapshot of model explanations at a fixed timepoint.”
Background
Prediction of Cognitive Test Scores from Variable Length Multimodal Data in Alzheimer’s Disease
Morar, U., Martin, H., M., R. P., Izquierdo, W., Zarafshan, E., Forouzannezhad, P., Unger, E., Cabrerizo, M., Curiel, R. E., Rosselli, M., et al. (2023) · Cognitive Computation · doi:10.1007/s12559-023-10169-w · Cited by 7
“The authors in study [34] took a different approach.”
Explainable Artificial Intelligence: A Survey of the Need, Techniques, Applications, and Future DirectionPreprint
Mersha, M. A., Lamb, K., Wood, J., Alshami, A. M., & Kalita, J. K. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4715286 · Cited by 7
Comparing machine learning classifier models in discriminating cognitively unimpaired older adults from three clinical cohorts in the Alzheimer’s disease spectrum: demonstration analyses in the COMPASS-ND study
Fah, H., Bohn, L., Greiner, R., & Dixon, R. A. (2025) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2025.1542514 · Cited by 7
Detecting label noise in longitudinal Alzheimer’s data with explainable artificial intelligence
Sorino, P., Lombardi, A., Lofù, D., Colafiglio, T., Ferrara, A., Narducci, F., Sciascio, E. D., & Noia, T. D. (2025) · Brain Informatics · doi:10.1186/s40708-025-00261-2 · Cited by 6
“El-Sappagh et al. [25] integrated XAI techniques into their predictive framework, utilizing SHAP, decision trees, and fuzzy rule-based systems to enhance interpretability.”
AlzFormer: Multi-modal framework for Alzheimer’s classification using MRI and graph-embedded demographics guided by adaptive attention gating
Hussain, S. S., Xu, D., Shah, P. M., Khan, H., & Zeb, A. (2025) · Computerized Medical Imaging and Graphics · doi:10.1016/j.compmedimag.2025.102638 · Cited by 8
Explainable Multi-Modal Fusion-Based Federated Learning for Mortality Prediction in Energy-Constrained Healthcare Systems
Bibi, M., Ahmad, R., Rizwan, A., Khan, A. N., Khan, Q. W., & Kim, D.-H. (2026) · IEEE Access · doi:10.1109/access.2025.3650610 · Cited by 9
“Studies have shown that XAI can enhance decision-making processes, as evidenced by frameworks developed for breast cancer treatment and Alzheimer’s disease diagnosis that offer interpretable, actionable insights through clustering and rule-based systems [14], [15].”
Alzheimer’s disease prediction via an explainable CNN using genetic algorithm and SHAP values
Zahedipour, M., Abadeh, M. S., & Shojaei, S. (2026) · PLoS ONE · doi:10.1371/journal.pone.0337800 · Cited by 6
“Another study proposed a multilayer multimodal detection and prediction model using MRI, PET, and genetic data with RF classifiers, achieving accuracies of 94.4% for AD detection and 86.8% for progression prediction, enhanced by SHAP and Decision Trees for explainability [28].”
Influential
Deep learning for multisource medical information processing
Gezimati, M., & Singh, G. K. (2024) · Elsevier eBooks · doi:10.1016/b978-0-44-313233-9.00009-6 · Cited by 6
Web-based artificial intelligence to predict cognitive impairment following stroke: A multicenter study
Hasan, F., Muhtar, M. S., Wu, D., Chen, P.-Y., Hsu, M., Nguyen, P., Chen, T., & Chiu, H. (2024) · Journal of Stroke and Cerebrovascular Diseases · doi:10.1016/j.jstrokecerebrovasdis.2024.107826 · Cited by 6
Advancements in Neurodegenerative Disease Diagnosis and Prediction: A Machine Learning Approach
Modi, S. K., Singla, S., Modi, P., & Kaur, G. K. (2025) · Lecture notes in networks and systems · doi:10.1007/978-981-97-7190-5_18 · Cited by 6
Explainable AI and Its Applications in Healthcare
Sarkar, A. (2022) · Intelligent systems reference library · doi:10.1007/978-3-031-12807-3_6 · Cited by 6
Explainable Artificial Intelligence: Concepts and Current Progression
Kangra, K., & Singh, J. (2022) · Studies in computational intelligence · doi:10.1007/978-3-031-18292-1_1 · Cited by 7
Unmasking Dementia Detection by Masking Input Gradients: A JSM Approach to Model Interpretability and Precision
Mustafa, Y., & Luo, T. (2024) · Lecture notes in computer science · doi:10.1007/978-981-97-2259-4_6 · Cited by 6
Explainable Artificial Intelligence in Endocrinological Medical Research
Webb‐Robertson, B. (2021) · The Journal of Clinical Endocrinology & Metabolism · doi:10.1210/clinem/dgab237 · Cited by 6
Towards inclusive explainable artificial intelligence: a thematic analysis and scoping review on tools for persons with disabilities
Atf, Z., & Lewis, P. R. (2025) · Disability and Rehabilitation Assistive Technology · doi:10.1080/17483107.2025.2507684 · Cited by 5
Computer-Aided Decision Support Systems of Alzheimer's Disease Diagnosis - A Systematic Review
Günaydın, T., & Varlı, S. (2025) · Current Medical Imaging Formerly Current Medical Imaging Reviews · doi:10.2174/0115734056359358250516101749 · Cited by 5
Explainable hierarchical machine-learning approaches for multimodal prediction of conversion from mild cognitive impairment to Alzheimer’s disease
Zarei, S., Saffar, M., Shalbaf, R., Hassani‐Abharian, P., & Shalbaf, A. (2025) · Physical and Engineering Sciences in Medicine · doi:10.1007/s13246-025-01618-x · Cited by 8
Explainable AI in Health Care: Trust and Transparency in AI-Powered Medical Diagnosis
Mohammed, Z. K. (2025) · Artificial intelligence · doi:10.5772/intechopen.1011279 · Cited by 9
Deep learning interpretability in neuroimaging: A comprehensive survey and methodological recommendations
Rahman, M. M., Calhoun, V. D., & Plis, S. (2026) · Imaging Neuroscience · doi:10.1162/imag.a.1129 · Cited by 16

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
ORCID: Islam, S. M. Riazul
Kwak, Kyung Sup
Venue: Scientific Reports
Publisher: Springer Science and Business Media LLC
Published: 2021-01-29
Volume: 11
Issue: 1
Pages: 2660-2660
ISSN: 2045-2322 (ISSN-L)
DOI: 10.1038/s41598-021-82098-3
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
Licence: cc-by
creativecommons.org/licenses/by/4.0 (tdm)
creativecommons.org/licenses/by/4.0 (vor)
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

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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

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