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

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 m...

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Autores principales:	Shaker El-Sappagh, Jose M. Alonso, S. M. Riazul Islam, Ahmad M. Sultan, Kyung Sup Kwak
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2021
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/c8e4d8f3940b4b0d82655cd0baa4c176
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spelling	oai:doaj.org-article:c8e4d8f3940b4b0d82655cd0baa4c1762021-12-02T13:24:25ZA multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease10.1038/s41598-021-82098-32045-2322https://doaj.org/article/c8e4d8f3940b4b0d82655cd0baa4c1762021-01-01T00:00:00Zhttps://doi.org/10.1038/s41598-021-82098-3https://doaj.org/toc/2045-2322Abstract 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.Shaker El-SappaghJose M. AlonsoS. M. Riazul IslamAhmad M. SultanKyung Sup KwakNature PortfolioarticleMedicineRScienceQENScientific Reports, Vol 11, Iss 1, Pp 1-26 (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Shaker El-Sappagh Jose M. Alonso S. M. Riazul Islam Ahmad M. Sultan Kyung Sup Kwak A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
description	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.
format	article
author	Shaker El-Sappagh Jose M. Alonso S. M. Riazul Islam Ahmad M. Sultan Kyung Sup Kwak
author_facet	Shaker El-Sappagh Jose M. Alonso S. M. Riazul Islam Ahmad M. Sultan Kyung Sup Kwak
author_sort	Shaker El-Sappagh
title	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
title_short	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
title_full	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
title_fullStr	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
title_full_unstemmed	A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease
title_sort	multilayer multimodal detection and prediction model based on explainable artificial intelligence for alzheimer’s disease
publisher	Nature Portfolio
publishDate	2021
url	https://doaj.org/article/c8e4d8f3940b4b0d82655cd0baa4c176
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A multilayer multimodal detection and prediction model based on explainable artificial intelligence for Alzheimer’s disease

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