Performance analysis on multimodal dataset.

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.

## 背景
据世界卫生组织（World Health Organization, WHO）统计，痴呆症是全球各类疾病中第七大致死原因，同时也是老年人群致残的主要诱因之一。阿尔茨海默病患者数量正逐年攀升，鉴于其发病率持续增长与潜在危害，该病的临床诊断需格外严谨。机器学习是阿尔茨海默病诊断的潜在技术路径，但由于多数机器学习模型存在黑箱特性，普通用户对其信任度不足；此外，部分模型仅依赖神经影像数据进行训练，未能实现最优性能表现。

## 目标
为解决上述问题，本文提出一种基于多模态数据集的可解释阿尔茨海默病预测模型。该方法通过数据级融合的方式整合临床数据、磁共振成像（Magnetic Resonance Imaging, MRI）分割数据与心理学数据。然而当前学界对阿尔茨海默病的多模态五分类任务的研究仍较为匮乏。

## 方法
针对五分类预测任务，本文选用了9种主流机器学习模型，分别为随机森林（Random Forest, RF）、逻辑回归（Logistic Regression, LR）、决策树（Decision Tree, DT）、多层感知机（Multi-Layer Perceptron, MLP）、K近邻（K-Nearest Neighbor, KNN）、梯度提升（Gradient Boosting, GB）、自适应提升（Adaptive Boosting, AdaB）、支持向量机（Support Vector Machine, SVM）以及朴素贝叶斯（Naive Bayes, NB）。其中随机森林模型取得了最高的综合评分。此外，本研究采用夏普利可加解释（SHapley Additive exPlanation, SHAP）方法实现模型的可解释性分析。

## 结果与结论
性能评估结果表明，随机森林分类器在阿尔茨海默病、认知正常、非阿尔茨海默型痴呆、疑似痴呆及其他类别的五分类任务中，10折交叉验证准确率可达98.81%。此外，本研究基于SHAP模型构建可解释人工智能框架，对预测结果的生成依据进行了系统分析。据我们所知，本研究首次利用开放获取影像研究系列（Open Access Series of Imaging Studies, OASIS-3）数据集，实现了融合临床、心理学及MRI分割数据的阿尔茨海默病多模态五分类任务。同时，本文还提出了一种新型阿尔茨海默病患者管理架构。