干旱区生态环境知识资源中心(Arid): Explainable AI-based Alzheimer's prediction and management using multimodal data

Arid

DOI	10.1371/journal.pone.0294253
	Explainable AI-based Alzheimer's prediction and management using multimodal data
	Jahan, Sobhana; Abu Taher, Kazi; Kaiser, M. Shamim; Mahmud, Mufti; Rahman, Md. Sazzadur; Hosen, A. S. M. Sanwar; Ra, In-Ho
通讯作者	Kaiser, MS
来源期刊	PLOS ONE
ISSN	1932-6203
出版年	2023
卷号	18 期号:11
英文摘要	BackgroundAccording to the World Health Organization (WHO), dementia is the seventh leading reason of death among all illnesses and one of the leading causes of disability among the world's elderly people. Day by day the number of Alzheimer's patients is rising. Considering the increasing rate and the dangers, Alzheimer's disease should be diagnosed carefully. Machine learning is a potential technique for Alzheimer's diagnosis but general users do not trust machine learning models due to the black-box nature. Even, some of those models do not provide the best performance because of using only neuroimaging data.ObjectiveTo solve these issues, this paper proposes a novel explainable Alzheimer's disease prediction model using a multimodal dataset. This approach performs a data-level fusion using clinical data, MRI segmentation data, and psychological data. However, currently, there is very little understanding of multimodal five-class classification of Alzheimer's disease.MethodFor predicting five class classifications, 9 most popular Machine Learning models are used. These models are Random Forest (RF), Logistic Regression (LR), Decision Tree (DT), Multi-Layer Perceptron (MLP), K-Nearest Neighbor (KNN), Gradient Boosting (GB), Adaptive Boosting (AdaB), Support Vector Machine (SVM), and Naive Bayes (NB). Among these models RF has scored the highest value. Besides for explainability, SHapley Additive exPlanation (SHAP) is used in this research work.Results and conclusionsThe performance evaluation demonstrates that the RF classifier has a 10-fold cross-validation accuracy of 98.81% for predicting Alzheimer's disease, cognitively normal, non-Alzheimer's dementia, uncertain dementia, and others. In addition, the study utilized Explainable Artificial Intelligence based on the SHAP model and analyzed the causes of prediction. To the best of our knowledge, we are the first to present this multimodal (Clinical, Psychological, and MRI segmentation data) five-class classification of Alzheimer's disease using Open Access Series of Imaging Studies (OASIS-3) dataset. Besides, a novel Alzheimer's patient management architecture is also proposed in this work.
类型	Article
语种	英语
开放获取类型	Green Submitted, gold, Green Published
收录类别	SCI-E
WOS记录号	WOS:001119675000107
WOS关键词	DISEASE
WOS类目	Multidisciplinary Sciences
WOS研究方向	Science & Technology - Other Topics
资源类型	期刊论文
条目标识符	http://119.78.100.177/qdio/handle/2XILL650/398137
推荐引用方式 GB/T 7714	Jahan, Sobhana,Abu Taher, Kazi,Kaiser, M. Shamim,et al. Explainable AI-based Alzheimer's prediction and management using multimodal data[J],2023,18(11).
APA	Jahan, Sobhana.,Abu Taher, Kazi.,Kaiser, M. Shamim.,Mahmud, Mufti.,Rahman, Md. Sazzadur.,...&Ra, In-Ho.(2023).Explainable AI-based Alzheimer's prediction and management using multimodal data.PLOS ONE,18(11).
MLA	Jahan, Sobhana,et al."Explainable AI-based Alzheimer's prediction and management using multimodal data".PLOS ONE 18.11(2023).