特征统计分析
基于OASIS-3的多模态阿尔茨海默病五分类数据集，含临床、MRI分割及心理数据。
基本信息
资源简介
特征统计分析
【数据集背景】
本数据集源站标题为《Statistical analysis of features.》。源站首次发布：2023-11-16。
【数据内容】
源站原始描述：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 bl…
【数据结构与技术规格】
文件数 1 个，合计 9.73 KB。Table 4.xls：application/vnd.ms-excel，9.73 KB。数据集 DOI：10.1371/journal.pone.0294253.t004。
【主题与分类】
源站学科分类：Neuroscience、Biological Sciences not elsewhere classified、Information Systems not elsewhere classified、Cancer、Science Policy、Mental Health。主题标签：world health organization、world &# 8217、shapley additive explanation、patient management architecture、alzheimer &# 8217、seventh leading reason、performance evaluation demonstrates、mlp ), k、dt ), multi、xlink "> according。
【适用方向】
多模态数据适合跨模态对齐、融合建模与联合表征学习方向的研究。
【获取与许可】
源站页面：https://plos.figshare.com/articles/dataset/Statistical_analysis_of_features_/24576189
使用许可：CC BY 4.0（https://creativecommons.org/licenses/by/4.0/）。
规范引用：Jahan, Sobhana; Abu Taher, Kazi; Kaiser, M. Shamim; Mahmud, Mufti; Rahman, Md. Sazzadur; Hosen, A. S. M. Sanwar; et al. (2023). Statistical analysis of features.. PLOS ONE. Dataset
下载信息
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使用方式
数据集获取（Figshare）
- 源站标题：Statistical analysis of features.
- DOI：10.1371/journal.pone.0294253.t004
- 发布日期：2023-11-16
- 文件数：1 个
- 文件总体积：9.73 KB
- 源站页面：https://plos.figshare.com/articles/dataset/Statistical_analysis_of_features_/24576189
命令行下载
curl -L -o "Table 4.xls" "https://ndownloader.figshare.com/files/43170336"
文件清单
Table 4.xls（9.73 KB）
文件总体积 9.73 KB（源站 API 实测）。
数据说明
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.
许可
CC BY 4.0
引用
学术使用请引用 DOI 10.1371/journal.pone.0294253.t004（source: https://plos.figshare.com/articles/dataset/Statistical_analysis_of_features_/24576189）。
需要用于商业化训练或医疗器械注册（NMPA/FDA）？
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