Explainable AI-based Alzheimer's prediction and management using multimodal data.

Autor: Jahan S; Department of Computer Science and Engineering, Bangladesh University of Professionals, Dhaka, Bangladesh.; Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh., Abu Taher K; Department of Information and Communication Technology, Bangladesh University of Professionals, Dhaka, Bangladesh., Kaiser MS; Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh., Mahmud M; Department of Computer Science, Nottingham Trent University, Nottingham, United Kingdom., Rahman MS; Institute of Information Technology, Jahangirnagar University, Savar, Dhaka, Bangladesh., Hosen ASMS; Department of Artificial Intelligence and Big Data, Woosong University, Daejeon, South Korea., Ra IH; School of Computer, Information and Communication Engineering, Kunsan National University, Gunsan, South Korea.
Jazyk: angličtina
Zdroj: PloS one [PLoS One] 2023 Nov 16; Vol. 18 (11), pp. e0294253. Date of Electronic Publication: 2023 Nov 16 (Print Publication: 2023).
DOI: 10.1371/journal.pone.0294253
Abstrakt: Background: According 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.
Objective: To 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.
Method: For 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 Conclusions: The 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.
Competing Interests: The authors have declared that no competing interests exist.
(Copyright: © 2023 Jahan et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)
Databáze: MEDLINE
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