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 model; their best performing model used SHAP feature attribution with random forest, and the most influential feature was the Mini-Mental State Examination.
“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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ExpliClas: Automatic Generation of Explanations in Natural Language for Weka Classifiers
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“In practice, both the activated branch of the DT and the winner rule of FURIA are translated into sequences of meaningful words (i.e., each numerical interval in the DT or fuzzy set in FURIA is verbalized by the closest linguistic term in ExpliClas).”
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There Is Hope After All: Quantifying Opinion and Trustworthiness in Neural Networks
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“Because AD is a complex disease and RF is a complex model, in order to have a global comprehensive, consistent, and accurate picture about AD progression, several explanatory techniques are required 88 .”
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“Regular ML techniques involving linear models and rule-induction algorithms (e.g., a decision tree [DT] 34 or a fuzzy rule-based system [FRBS] 35,36 ) are usually preferred when the priority is to generate explainable models 18,37–39 .”
BackgroundModeling Interpretable Fuzzy Rule-Based Classifiers for Medical Decision Support
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Hybrid Data-Expert Explainable Beer Style Classifier
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Self-citationTau protein abnormalities correlate with the severity of dementia in Alzheimer's disease
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Ali, S., Abuhmed, T., El-Sappagh, S., Muhammad, K., Alonso-Moral, J. M., Confalonieri, R., Guidotti, R., Del Ser, J., Díaz-Rodríguez, N., & Herrera, F. (2023) · Information Fusion · doi:10.1016/j.inffus.2023.101805 · Cited by 1,762 · see citing works
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A Review of Explainable Artificial Intelligence in Healthcare
Sadeghi, Z., Alizadehsani, R., CIFCI, M. A., Kausar, S., Rehman, R., Mahanta, P., Bora, P. K., Almasri, A., Alkhawaldeh, R. S., Hussain, S., et al. (2024) · Computers and Electrical Engineering · doi:10.1016/j.compeleceng.2024.109370 · Cited by 598 · see citing works
Multimodal machine learning in precision health: A scoping review
Kline, A., Wang, H., Li, Y., Dennis, S., Hutch, M., Xu, Z., Wang, F., Cheng, F., & Luo, Y. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00712-8 · Cited by 528 · see citing works
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“EI-Sappagh et al. suggested an RF model for detecting Alzheimer’s disease (AD) progression and diagnosis in [215].”
MethodApplications of Explainable Artificial Intelligence in Diagnosis and Surgery
Zhang, Y., Weng, Y., & Lund, J. (2022) · Diagnostics · doi:10.3390/diagnostics12020237 · Cited by 368 · see citing works
“4 [27] 2021 Alzheimer’s disease Two-layer model with RF First layer: accuracy: 93.”
Mersha, M. A., Lâm, K. N., Wood, J., AlShami, A. K., & Kalita, J. K. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128111 · Cited by 302 · see citing works
Machine learning in point-of-care testing: innovations, challenges, and opportunities
Han, G., Goncharov, A., Eryılmaz, M., Ye, S., Palanisamy, B., Ghosh, R., Lisi, F., Rogers, E., Guzmán, D., Yigci, D., et al. (2025) · Nature Communications · doi:10.1038/s41467-025-58527-6 · Cited by 262 · see citing works
“In another application, EI-Sappagh et al. applied SHAP to evaluate the impact of patient features on Alz-heimer ’ s disease progression over time 202 .”
Alkhanbouli, R., Almadhaani, H. M. A., Alhosani, F., & Simsekler, M. C. E. (2025) · BMC Medical Informatics and Decision Making · doi:10.1186/s12911-025-02944-6 · Cited by 264 · see citing works
“Notably, genetic programming, as reported in [15] has shown promise in the diagnosis of sarcoidosis, while the implementation of Fuzzy logic has been instrumental in evaluating Alzheimer’s Disease, as discussed in [21].”
A survey on the interpretability of deep learning in medical diagnosis
Teng, Q., Liu, Z., Song, Y., Han, K., & Lu, Y. (2022) · Multimedia Systems · doi:10.1007/s00530-022-00960-4 · Cited by 212 · see citing works
“Moreover, a multilayer multimodal model was proposed [83] to diagnose AD and detect its progression.”
BackgroundArtificial Intelligence for Clinical Prediction: Exploring Key Domains and Essential Functions
Khalifa, M., & Albadawy, M. (2024) · Computer Methods and Programs in Biomedicine Update · doi:10.1016/j.cmpbup.2024.100148 · Cited by 201 · see citing works
Explainable artificial intelligence in Alzheimer’s disease classification: a systematic review
Viswan, V., Shaffi, N., Mahmud, M., Subramanian, K., & Hajamohideen, F. (2024) · Cognitive Computation · doi:10.1007/s12559-023-10192-x · Cited by 195 · see citing works
“According to the study in [122], the output decision is supported by several complimentary, credible, and visible justifications.”
InfluentialKale, M. B., Wankhede, N. L., Pawar, R. S., Ballal, S., Kumawat, R., Goswami, M., Khalid, M., Taksande, B. G., Upaganlawar, A. B., Umekar, M. J., et al. (2024) · Ageing Research Reviews · doi:10.1016/j.arr.2024.102497 · Cited by 199 · see citing works
Recent Applications of Explainable AI (XAI): A Systematic Literature Review
Saarela, M., & Podgorelec, V. (2024) · Applied Sciences · doi:10.3390/app14198884 · Cited by 219 · see citing works
From Blackbox to Explainable AI in Healthcare: Existing Tools and Case Studies
Srinivasu, P. N., Sandhya, N., Jhaveri, R. H., & Raut, R. (2022) · Mobile Information Systems · doi:10.1155/2022/8167821 · Cited by 187 · see citing works
An explainable machine learning approach for Alzheimer’s disease classification
Alatrany, A. S., Khan, W., Hussain, A., Kolivand, H., & Al-Jumeily, D. (2024) · Scientific Reports · doi:10.1038/s41598-024-51985-w · Cited by 172 · see citing works
“A two-layer RF model approach was proposed in study 21 for diagnosis and progression detection of AD.”
Domenico, M. D., Allegri, L., Caldarelli, G., d’Andrea, V., Camillo, B. D., Rocha, L. M., Rozum, J. C., Sbarbati, R., & Zambelli, F. (2025) · npj Digital Medicine · doi:10.1038/s41746-024-01402-3 · Cited by 184 · see citing works
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Nazih, W., Aseeri, A. O., Atallah, O. Y., & El–Sappagh, S. (2023) · IEEE Access · doi:10.1109/access.2023.3326528 · Cited by 158 · see citing works
Self-citationArtificial Intelligence in Biological Sciences
Bhardwaj, A., Kishore, S., & Pandey, D. K. (2022) · Life · doi:10.3390/life12091430 · Cited by 165 · see citing works
Artificial Intelligence for Alzheimer’s Disease: Promise or Challenge?
Fabrizio, C., Termine, A., Caltagirone, C., & Sancesario, G. M. (2021) · Diagnostics · doi:10.3390/diagnostics11081473 · Cited by 156 · see citing works
“The model achieved a good performance while balancing the accuracy–interpretability trade-off in both AD classification and MCI-to-AD prediction tasks, allowing for actionable decisions that can enhance physician confidence, contributing to the realization of explainable AI (XAI) in healthcare [79].”
BackgroundMulti-modality approaches for medical support systems: A systematic review of the last decade
Salvi, M., Loh, H. W., Seoni, S., Barua, P. D., García, S., Molinari, F., & Acharya, U. R. (2024) · Information Fusion · doi:10.1016/j.inffus.2023.102134 · Cited by 144 · see citing works
“Visualization approaches to illustrate model attention mechanisms, feature relevance, and internal representations may also prove useful for practitioners to distill multimodal model behaviors [30].”
BackgroundMethodInfluentialElmannai, H., El-Rashidy, N., Mashal, I., Alohali, M. A., Farag, S., El-Sappagh, S., & Saleh, H. (2023) · Diagnostics · doi:10.3390/diagnostics13081506 · Cited by 140 · see citing works
“Explainability is motivated by the lack of model transparency of complex (black-box) models that lack model trust [14].”
BackgroundSelf-citationArtificial intelligence for biomarker discovery in Alzheimer's disease and dementia
Winchester, L. M., Harshfield, E. L., Shi, L., Badhwar, A., Khleifat, A. A., Clarke, N., Dehsarvi, A., Lengyel, I., Lourida, I., Madan, C. R., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13390 · Cited by 130 · see citing works
“27 Ultimately, policy and regulation must be based on outcomes for patients.”
BackgroundKibria, H. B., Nahiduzzaman, M., Goni, M. O. F., Ahsan, M., & Haider, J. (2022) · Sensors · doi:10.3390/s22197268 · Cited by 132 · see citing works
“It combines some other tools, such as LIME and many more [38].”
MethodExplainable AI approaches in deep learning: Advancements, applications and challenges
Hosain, M. T., Jim, J. R., Mridha, M. F., & Kabir, M. (2024) · Computers & electrical engineering · doi:10.1016/j.compeleceng.2024.109246 · Cited by 119 · see citing works
Rahim, N., El–Sappagh, S., Ali, S., Muhammad, K., Ser, J. D., & Abuhmed, T. (2022) · Information Fusion · doi:10.1016/j.inffus.2022.11.028 · Cited by 126 · see citing works
Self-citationExplainable AI-based Alzheimer’s prediction and management using multimodal data
Jahan, S., Abu Taher, K., Kaiser, M. S., Mahmud, M., Rahman, M. S., Hosen, A. S. M. S., & Ra, I.-H. (2023) · PLOS ONE · doi:10.1371/journal.pone.0294253 · Cited by 122 · see citing works
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Explainable AI for Credit Assessment in Banks
de Lange, P. E., Melsom, B., Vennerød, C. B., & Westgaard, S. (2022) · Journal of risk and financial management · doi:10.3390/jrfm15120556 · Cited by 107 · see citing works
Explainable, Domain-Adaptive, and Federated Artificial Intelligence in Medicine
Chaddad, A., Lu, Q., Li, J., Katib, Y., Kateb, R., Tanougast, C., Bouridane, A., & Abdulkadir, A. (2023) · IEEE/CAA Journal of Automatica Sinica · doi:10.1109/jas.2023.123123 · Cited by 112 · see citing works
“Preclinical relevance assessment Model combination / G GNN, FNN [89]”
BackgroundHeuillet, A., Couthouis, F., & Díaz-Rodríguez, N. (2022) · IEEE Computational Intelligence Magazine · doi:10.1109/mci.2021.3129959 · Cited by 105 · see citing works
El–Sappagh, S., Ali, F., Abuhmed, T., Singh, J., & Alonso, J. M. (2022) · Neurocomputing · doi:10.1016/j.neucom.2022.09.009 · Cited by 87 · see citing works
“Moreover, in the medical domain, it is not easy to introduce novel ML methods while physicians are asking for methods that are multi-modal with comprehensible recommendations [32].”
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Explainable AI: A review of applications to neuroimaging data
Farahani, F. V., Fiok, K., Lahijanian, B., Karwowski, W., & Douglas, P. K. (2022) · Frontiers in Neuroscience · doi:10.3389/fnins.2022.906290 · Cited by 85 · see citing works
Alsalem, M. A., Alamoodi, A. H., Albahri, O. S., Albahri, A. S., Martínez, L. M., Yera, R., Duhaim, A. M., & Sharaf, I. M. (2024) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.123066 · Cited by 80 · see citing works
Lombardi, A., Diacono, D., Amoroso, N., Biecek, P., Monaco, A., Bellantuono, L., Pantaleo, E., Logroscino, G., Blasi, R. D., Tangaro, S., et al. (2022) · Brain Informatics · doi:10.1186/s40708-022-00165-5 · Cited by 76 · see citing works
“El-Sappagh and collegues [37] developed a ML model for AD diagnosis and progression detection by integrating 11 modalities of 1048 subjects from the Alzheimer’s Disease Neuroimaging Initiative (ADNI) dataset.”
MethodXAI-Based Clinical Decision Support Systems: A Systematic Review
Kim, S. Y., Kim, D. H., Kim, M., Ko, H.-J., & Jeong, O. (2024) · Applied Sciences · doi:10.3390/app14156638 · Cited by 84 · see citing works
Lu, P., Hu, L., Mitelpunkt, A., Bhatnagar, S., Lu, L. J., & Liang, H. (2023) · Biomedical Signal Processing and Control · doi:10.1016/j.bspc.2023.105669 · Cited by 73 · see citing works
In-depth insights into Alzheimer’s disease by using explainable machine learning approach
Bogdanović, B., Eftimov, T., & Simjanoska, M. (2021) · Scientific Reports · doi:10.1038/s41598-022-10202-2 · Cited by 70 · see citing works
Explainable Artificial Intelligence in Neuroimaging of Alzheimer’s Disease
Taiyeb Khosroshahi, M., Morsali, S., Gharakhanlou, S., Motamedi, A., Hassanbaghlou, S., Vahedi, H., Pedrammehr, S., Kabir, H. M. D., & Jafarizadeh, A. (2025) · Diagnostics · doi:10.3390/diagnostics15050612 · Cited by 68 · see citing works
“In the research field related to AD, a study by El-sappagh et al. [100] aimed to provide a comprehensive understanding of how various modalities influence the risk of AD.”
Nagahisarchoghaei, M., Nur, N., Cummins, L., Nur, N., Karimi, M. M., Nandanwar, S., Bhattacharyya, S., & Rahimi, S. (2023) · Electronics · doi:10.3390/electronics12051092 · Cited by 76 · see citing works
Explainable Artificial Intelligence Based Framework for Non-Communicable Diseases Prediction
Davagdorj, K., Bae, J., Pham, V.-H., Theera‐Umpon, N., & Ryu, K. H. (2021) · IEEE Access · doi:10.1109/access.2021.3110336 · Cited by 61 · see citing works
“According to the study [33], the authors developed an accurate and interpretable Alzheimer’s disease diagnosis and progression detection model.”
MethodA Novel AI-Based System for Detection and Severity Prediction of Dementia Using MRI
Jain, V., Nankar, O., Jerrish, D. J., Gite, S., Patil, S., & Kotecha, K. V. (2021) · IEEE Access · doi:10.1109/access.2021.3127394 · Cited by 62 · see citing works
“[41] proposed a Machine learning model using random forest classification.”
MethodAlMohimeed, A., Saad, R. M. A., Mostafa, S., El-Rashidy, N. M., Farrag, S., Gaballah, A., Elaziz, M. A., El–Sappagh, S., & Saleh, H. (2023) · IEEE Access · doi:10.1109/access.2023.3328331 · Cited by 67 · see citing works
“Multimodal baseline data and ML/DL models: It is expected that integrating heterogeneous multimodal data (e.g., neuroimages, lab tests, memory tests, genetics, etc.) will enhance the performance of ML models and support tailored and customized decision-making [13], [50], [51], [52].”
Self-citationAdarsh, V., Gangadharan, G. R., Fiore, U., & Zanetti, P. (2024) · Scientific Reports · doi:10.1038/s41598-024-52185-2 · Cited by 59 · see citing works
“Using binary categorisation and layer-by-layer data analysis, El-Sappagh et al. 29 present a highly accurate and interpretable machine-learning model for AD diagnosis and progression detection.”
MethodBi, Y., Abrol, A., Fu, Z., & Calhoun, V. D. (2024) · Human Brain Mapping · doi:10.1002/hbm.26783 · Cited by 64 · see citing works
Scheda, R., & Diciotti, S. (2022) · Applied Sciences · doi:10.3390/app12136681 · Cited by 56 · see citing works
“Previously, SHAP values were estimated for the test set of a single nCV repetition, thus generating potentially unstable explanations [36,66,67].”
MethodUnveiling Explainable <scp>AI</scp> in Healthcare: Current Trends, Challenges, and Future Directions
Noor, A. A., Manzoor, A., Mazhar Qureshi, M. D., Qureshi, M. A., & Rashwan, W. (2025) · WIREs Data Mining and Knowledge Discovery · doi:10.1002/widm.70018 · Cited by 52 · see citing works
El–Sappagh, S., Alonso, J. M., Abuhmed, T., Ali, F., & Bugarín, A. (2023) · Artificial Intelligence Review · doi:10.1007/s10462-023-10415-5 · Cited by 54 · see citing works
Self-citationWang, C., Li, Y., Tsuboshita, Y., Sakurai, T., Goto, T., Yamaguchi, H., Yamashita, Y., Sekiguchi, A., Tachimori, H., Initiative, F. T. A. D. N., et al. (2022) · npj Digital Medicine · doi:10.1038/s41746-022-00577-x · Cited by 51 · see citing works
“Random forest[17] NA-ADNI MRI, PET, Cognitive scores, APOE 3 years 0.”
MethodHajjo, R., Sabbah, D. A., Abusara, O. H., & Bawab, A. Q. A. (2022) · Diagnostics · doi:10.3390/diagnostics12122975 · Cited by 55 · see citing works
Alzheimer’s Disease Detection from Fused PET and MRI Modalities Using an Ensemble Classifier
Shukla, A., Tiwari, R., & Tiwari, S. (2023) · Machine Learning and Knowledge Extraction · doi:10.3390/make5020031 · Cited by 50 · see citing works
Martínez-Agüero, S., Soguero-Ruíz, C., Alonso, J. M., Mora-Jiménez, I., Álvarez-Rodríguez, J., & Marques, A. G. (2022) · Future Generation Computer Systems · doi:10.1016/j.future.2022.02.021 · Cited by 47 · see citing works
“…clinical models need to be endowed with interpretability as a requirement to become explainable, trustworthy, and used worldwide [21]. a Indeed, interpretability is of paramount importance to make intelligent systems ready to assist clinicians in high-risk decision-making processes [20].”
BackgroundSelf-citationRamírez‐Mena, A., Andrés‐León, E., Álvarez-Cubero, M. J., Anguita‐Ruiz, A., Martínez-González, L. J., & Alcalá‐Fdez, J. (2023) · Computer Methods and Programs in Biomedicine · doi:10.1016/j.cmpb.2023.107719 · Cited by 48 · see citing works
Qi, C., Wu, M., Zheng, J., Chen, Q., & Chai, L. (2022) · Journal of Cleaner Production · doi:10.1016/j.jclepro.2022.130958 · Cited by 47 · see citing works
Mechanistic Insights for Drug Repurposing and the Design of Hybrid Drugs for Alzheimer’s Disease
Padhi, D., & Govindaraju, T. (2022) · Journal of Medicinal Chemistry · doi:10.1021/acs.jmedchem.2c00335 · Cited by 47 · see citing works
Multilayer dynamic ensemble model for intensive care unit mortality prediction of neonate patients
Juraev, F., El–Sappagh, S., Abdukhamidov, E., Ali, F., & Abuhmed, T. (2022) · Journal of Biomedical Informatics · doi:10.1016/j.jbi.2022.104216 · Cited by 44 · see citing works
“Medically, XAI features were implemented to ex-4 plain decisions for diagnosis and progression detection of many diseases like Alzheimer ’ s disease [96].”
MethodSelf-citationArtificial Intelligence Models in the Diagnosis of Adult-Onset Dementia Disorders: A Review
Battineni, G., Chintalapudi, N., Hossain, M. A., Losco, G., Ruocco, C., Sagaro, G. G., Traini, E., Nittari, G., & Amenta, F. (2022) · Bioengineering · doi:10.3390/bioengineering9080370 · Cited by 43 · see citing works
“In addition, two studies used RF algorithms [45,51], whereas the remaining eight used multiple AI classifiers [46–48,52–55,57].”
MethodArtificial intelligence for dementia prevention
Newby, D., Orgeta, V., Marshall, C. R., Lourida, I., Albertyn, C. P., Tamburin, S., Raymont, V., Veldsman, M., Koychev, I., Bauermeister, S., et al. (2023) · Alzheimer's & Dementia · doi:10.1002/alz.13463 · Cited by 42 · see citing works
“109 A good example of this is DNNs, which are powerful and flexible in practice, although incorporate hidden layers, which have led them to be described as a “black box” technique.”
BackgroundArtificial intelligence for dementia research methods optimization
Bucholc, M., James, C. R., Khleifat, A. A., Badhwar, A., Clarke, N., Dehsarvi, A., Madan, C. R., Marzi, S. J., Shand, C., Schilder, B. M., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13441 · Cited by 44 · see citing works
“26 models with low interpretability; trust can be improved with transparent, explainable approaches [146], and reporting an algorithm’s confidence in its decision [147].”
BackgroundTracing the Influence of Large Language Models across the Most Impactful Scientific Works
Petroșanu, D.-M., Pîrjan, A., & Tăbușcă, A. (2023) · Electronics · doi:10.3390/electronics12244957 · Cited by 41 · see citing works
Explainable AI to Predict Male Fertility Using Extreme Gradient Boosting Algorithm with SMOTE
Roy, D. G., Alvi, P. A., & Santosh, K. (2022) · Electronics · doi:10.3390/electronics12010015 · Cited by 40 · see citing works
“Due to these reasons, AI systems need to be comprehensible and interpretable to analyze data and make judgments that would enhance patient care [10,11].”
BackgroundMoguilner, S., Birba, A., Fittipaldi, S., Gonzalez-Campo, C., Tagliazucchi, E., Reyes, P. A., Matallana, D., Parra, M. A., Slachevsky, A., Farias, G. A., et al. (2022) · Journal of Neural Engineering · doi:10.1088/1741-2552/ac87d0 · Cited by 42 · see citing works
González-Alday, R., García-Cuesta, E., Maojo, & Kulikowski, C. (2023) · Applied Sciences · doi:10.3390/app131910778 · Cited by 38 · see citing works
Bhandarkar, A., Naik, P., Vakkund, K., Junjappanavar, S., Bakare, S., & Pattar, S. (2024) · Artificial Intelligence Review · doi:10.1007/s10462-023-10644-8 · Cited by 47 · see citing works
Wang, C., Tachimori, H., Yamaguchi, H., Sekiguchi, A., Li, Y., Yamashita, Y., & Initiative, F. A. D. N. (2024) · Translational Psychiatry · doi:10.1038/s41398-024-02819-w · Cited by 39 · see citing works
Vlontzou, M. E., Αθανασίου, Μ., Dalakleidi, K. V., Skampardoni, I., Davatzikos, C. A., & Nikita, K. S. (2025) · Scientific Reports · doi:10.1038/s41598-025-92577-6 · Cited by 42 · see citing works
“Moreover, the SHapley Additive exPlanations (SHAP) interpretability method has been utilised towards the identification of highly influential features for the models’ predictions 6,8–11 .”
Hernández, M., Ramon-Julvez, U., & Ferraz, F. M. (2022) · PLoS ONE · doi:10.1371/journal.pone.0264695 · Cited by 40 · see citing works
“[24] recently proposed the use of SHAP values in a random-forest-based multilayer multimodal detection and prediction model.”
Sharma, D., Gotlieb, N., Farkouh, M. E., Patel, K., Xu, W., & Bhat, M. (2022) · Journal of the American Heart Association · doi:10.1161/jaha.121.022576 · Cited by 36 · see citing works
Federated Learning of XAI Models in Healthcare: A Case Study on Parkinson’s Disease
Ducange, P., Marcelloni, F., Renda, A., & Ruffini, F. (2024) · Cognitive Computation · doi:10.1007/s12559-024-10332-x · Cited by 36 · see citing works
Martí‐Juan, G., Lorenzi, M., & Piella, G. (2023) · NeuroImage · doi:10.1016/j.neuroimage.2023.119892 · Cited by 35 · see citing works
Artificial intelligence for neurodegenerative experimental models
Marzi, S. J., Schilder, B. M., Nott, A., Frigerio, C. S., Willaime‐Morawek, S., Bucholc, M., Hanger, D. P., James, C. R., Lewis, P. A., Lourida, I., et al. (2023) · Alzheimer s & Dementia · doi:10.1002/alz.13479 · Cited by 34 · see citing works
Calderón-Díaz, M., Aguirre, R. S., Vásconez, J. P., Yáñez, R., Roby, M., Querales, M., & Rodrigo Salas, F. (2023) · Sensors · doi:10.3390/s24010119 · Cited by 41 · see citing works
Caterson, J., Lewin, A., & Williamson, E. (2024) · Digital Health · doi:10.1177/20552076241272657 · Cited by 40 · see citing works
Multimodal missing data in healthcare: A comprehensive review and future directions
Le, L. P., Nguyen, T., Riegler, M., Halvorsen, P., & Nguyen, B. T. (2025) · Computer Science Review · doi:10.1016/j.cosrev.2024.100720 · Cited by 39 · see citing works
El-Rashidy, N. M., ElSayed, N. E., El-Ghamry, A., & Talaat, F. M. (2022) · Soft Computing · doi:10.1007/s00500-022-07420-1 · Cited by 35 · see citing works
“The two main types of AI explainability include a global method that is used to understand the overall behavior of the model and the effect of each feature in the output decision and a local method is used to clarify the decision of the model for each instance (El-Sappagh et al. 2021).”
MethodRetractedPhysiological signal analysis using explainable artificial intelligence: A systematic review
Shen, J. G., Wu, J., Liang, H., Zhao, Z., Li, K., Zhu, K., Wang, K., Ma, Y., Hu, W., Guo, C., et al. (2024) · Neurocomputing · doi:10.1016/j.neucom.2024.128920 · Cited by 35 · see citing works
Hafeez, Y., Memon, K., al-Quraishi, M., Yahya, N., Elferik, S., & Ali, S. S. A. (2025) · Diagnostics · doi:10.3390/diagnostics15020168 · Cited by 37 · see citing works
“Currently, XAI seems to be in its infancy, and most of the energy and attention of researchers and data scientists is focused on the accuracy enhancement and performance optimization in CAD tools [100].”
A comprehensive review of explainable AI for disease diagnosis
Biswas, A. A. (2024) · Array · doi:10.1016/j.array.2024.100345 · Cited by 33 · see citing works
Chen, Z., Yi, G., Li, X., Yi, B., Bao, X., Zhang, Y., Zhang, X., Yang, Z., & Guo, Z. (2024) · BMC Cancer · doi:10.1186/s12885-024-13098-5 · Cited by 31 · see citing works
A Roadmap of Explainable Artificial Intelligence: Explain to Whom, When, What and How?
Wang, Z., Huang, C., & Yao, X. (2024) · ACM Transactions on Autonomous and Adaptive Systems · doi:10.1145/3702004 · Cited by 32 · see citing works
El-Rashidy, N. M., ElSayed, N. E., El-Ghamry, A., & Talaat, F. M. (2022) · Neural Computing and Applications · doi:10.1007/s00521-022-08007-5 · Cited by 31 · see citing works
RetractedAn eXplainability Artificial Intelligence approach to brain connectivity in Alzheimer's disease
Amoroso, N., Quarto, S., Rocca, M. L., Tangaro, S., Monaco, A., & Bellotti, R. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1238065 · Cited by 31 · see citing works
“The use of XAI methods to characterize neurodegenerative diseases and, more in general, to equip neuroimaging studies is rapidly increasing (El-Sappagh et al., 2021; Anjomshoae and Pudas, 2022; Lombardi et al., 2022).”
MethodArtificial Intelligence Techniques for the effective diagnosis of Alzheimer’s Disease: A Review
Shastry, K. A., & Sanjay, H. A. (2023) · Multimedia Tools and Applications · doi:10.1007/s11042-023-16928-z · Cited by 31 · see citing works
“Both in AD diagnosis and MCI-to-AD prognostication tests, the method performs well while trying to balance the accuracy–interpretability trade-off, enabling for practical judgments that could boost doctor trust while leading to the implementation of "explainable AI (XAI)" in medicine [116].”
Unveiling Explainable AI in Healthcare: Current Trends, Challenges, and Future Directions
Aziz, N. A., Manzoor, A., Mazhar Qureshi, M. D., Qureshi, M. A., & Rashwan, W. (2024) · doi:10.1101/2024.08.10.24311735 · Cited by 30 · see citing works
“…of the datasets relevant to each application area are presented in Table 8 and 9, Img (1) [23] Txt (4) [24] Prv (12) Tab (11) [25], [26], [27], [28], [29] , [30], [31], [22], [32], [21], [33] Img (1) [34] Cancer (13) Pub (7) Tab (4) [21], [35]–[37] Img (3) [38]–[40] Prv (6) Tab (3) [33], [41],…”
BackgroundMethodInfluentialPoonia, R. C., & Al-Alshaikh, H. A. (2024) · Computers in Biology and Medicine · doi:10.1016/j.compbiomed.2024.108874 · Cited by 31 · see citing works
Sarica, A., Aracri, F., Bianco, M. G., Arcuri, F., Quattrone, A., Quattrone, A., & Initiative, A. D. N. (2023) · Brain Informatics · doi:10.1186/s40708-023-00211-w · Cited by 28 · see citing works
“Artificial Intelligence (AI) and Machine Learning (ML) reached excellent accuracy for the early diagnosis of AD [4–6] and for the prediction of progression from MCI to AD [5, 7, 8], showing also good interpretability and explainability [9, 10].”
BackgroundTekkesinoglu, S., Pudas, S., & (ADNI), A. D. N. I. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2023.1334613 · Cited by 28 · see citing works
“Post-hoc explanation methods including SHapley Additive exPlanations (SHAP) (Danso et al., 2021; El-Sappagh et al., 2021; Lombardi et al., 2022), Local Interpretable Model-agnostic Explanations (LIME) (Kamal et al., 2021; Ilias and Askounis, 2022), time series grad-cam (Rahim et al., 2022), feature…”
BackgroundMethodYue, L., Chen, W.-G., Liu, S., Chen, S., & Xiao, S. (2023) · Frontiers in Aging Neuroscience · doi:10.3389/fnagi.2023.1267020 · Cited by 27 · see citing works
Explainable AI for Sensor Signal Interpretation to Revolutionize Human Health Monitoring: A Review
Alharthi, A. S., Alqurashi, A., Alharbi, T. E. A., Alammar, M. M., Aldosari, N., Bouchekara, H. R. E. H., Sha’aban, Y. A., Shahriar, M. S., & Ayidh, A. A. (2025) · IEEE Access · doi:10.1109/access.2025.3585764 · Cited by 35 · see citing works
“SHAP explanation framework applied to multi-modal data, illustrating both local and global attributions for a Random Forest model in Alzheimer’s disease prediction [136]. disorder detection, further improving diagnostic clarity and performance.”
Influentialde Erausquin, G. A., Snyder, H. M., Brugha, T. S., Seshadri, S. S., Carrillo, M. C., Sagar, R., Huang, Y., Newton, C. R. J. C., Tartaglia, M. C., Teunissen, C. E., et al. (2022) · Alzheimer s & Dementia Translational Research & Clinical Interventions · doi:10.1002/trc2.12348 · Cited by 26 · see citing works
Battineni, G., Chintalapudi, N., & Amenta, F. (2024) · JMIR Aging · doi:10.2196/59370 · Cited by 26 · see citing works
“Supervised models like SVM were used by several studies for classification tasks due to their effectiveness in handling high-dimensional magnetic resonance image data and nonlinear relationships [53,54,58,64,67-69, 71, 75].”
InfluentialExplainable AI in Digestive Healthcare and Gastrointestinal Endoscopy
Mascarenhas, M., Mendes, F., Martins, M., Ribeiro, T., Afonso, J. P. L., Cardoso, P., Ferreira, J. P. S., Fonseca, J., & Macedo, G. (2025) · Journal of Clinical Medicine · doi:10.3390/jcm14020549 · Cited by 30 · see citing works
“…(e.g., patient history, human papilloma virus status, and previous treatment responses), a comprehensive analysis can inform treatment decisions [43]. on risk, aligned with lesion severity and characteristics, XAI could recommend monitoring low-grade lesions, treatment of HSIL, or surgical…”
Bloch, L., Friedrich, C. M., & Initiative, A. D. N. (2022) · SN Computer Science · doi:10.1007/s42979-022-01371-y · Cited by 33 · see citing works
El-Rashidy, N. M., Sedik, A., Siam, A. I., & Ali, Z. H. (2023) · Neural Computing and Applications · doi:10.1007/s00521-023-08258-w · Cited by 27 · see citing works
“Generally, models that have a good performance in terms of classification accuracy as SVM and LDA are not able to provide a clear explanation about the output decision [73], whereas the most tree-based algorithms are very good explainability, but may not be the best algorithm in terms of performance [74], and (iii) RF…”
BackgroundMethodBarnard, L., Botha, H., Corriveau-Lecavalier, N., Graff-Radford, J., Dicks, E., Gogineni, V., Zhang, G., Burkett, B. J., Johnson, D. R., Huls, S. J., et al. (2025) · Neurology · doi:10.1212/wnl.0000000000213831 · Cited by 25 · see citing works
Prediction Models for Early Detection of Alzheimer: Recent Trends and Future Prospects
Kaur, I., & Sachdeva, R. (2025) · Archives of Computational Methods in Engineering · doi:10.1007/s11831-025-10246-3 · Cited by 28 · see citing works
“The model explainability are also added using SHAP and LIME frameworks in recent studies [80, 81].”
- 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
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