Early and accurate detection of Alzheimer's disease (AD) remains a major challenge in medical diagnosis due to its subtle onset and progressive nature. This research introduces an explainable ensemble learning Framework designed to classify individuals as Alzheimer's or Non-Alzheimer's using structured clinical, lifestyle, metabolic, and lifestyle features. The workflow incorporates rigorous preprocessing, advanced feature engineering, SMOTE-Tomek hybrid class balancing, and optimized modeling using five ensemble algorithms-Random Forest, XGBoost, LightGBM, CatBoost, and Extra Trees-alongside a deep artificial neural network. Model selection was performed using stratified validation to prevent leakage, and the best-performing model was evaluated on a fully unseen test set. Ensemble methods achieved superior performance over deep learning, with XGBoost, Random Forest, and Soft Voting showing the strongest accuracy, sensitivity, and F1-score profiles. Explainability techniques, including SHAP and feature importance analysis, highlighted MMSE, Functional Assessment Age, and several engineered interaction features as the most influential determinants.
The results demonstrate that the proposed framework provides a reliable and transparent approach to Alzheimer's disease prediction, offering strong potential for clinical decision support applications.
Cognitive Threat Intelligence and Explainable Federated Security Analytics for distributed Infrastructure Systems
Dept. of Information Studies, Trine University（信息研究系，特林大学）；Dept. of Business Information Systems, Central Michigan University（商业信息系统系，中央密歇根大学）；Dept. of CS, Maharishi International University（计算机科学系， Maharishi 国际大学）
Alzheimer's Disease (AD) is a progressive neurodegenerative disease and the leading cause of dementia. Early diagnosis is critical for patients to benefit from potential intervention and treatment. The retina has been hypothesized as a diagnostic site for AD detection owing to its anatomical connection with the brain. Developed AI models for this purpose have yet to provide a rational explanation about the decision and neither infer the stage of disease's progression. Along this direction, we propose a novel model-agnostic explainable-AI framework, called Granular Neuron-level Explainer (LAVA)…
University of Applied Sciences and Arts Dortmund（多特蒙德应用科学与艺术大学）；University Hospital Essen（埃森大学医院）
Purpose: Hard-to-interpret Black-box Machine Learning (ML) were often used for early Alzheimer's Disease (AD) detection. Methods: To interpret eXtreme Gradient Boosting (XGBoost), Random Forest (RF), and Support Vector Machine (SVM) black-box models a workflow based on Shapley values was developed. All models were trained on the Alzheimer's Disease Neuroimaging Initiative (ADNI) dataset and evaluated for an independent ADNI test set, as well as the external Australian Imaging and Lifestyle flagship study of Ageing (AIBL), and Open Access Series of Imaging Studies (OASIS) datasets. Shapley valu…
Early Detection of Alzheimer's Disease Using Explainable Machine Learning on Clinical Biomarkers: A Multi-Class Classification Study Using the Alzheimer's Disease Neuroimaging Initiative (ADNI) Dataset
Diagnosing Alzheimer's disease (AD) involves a deliberate diagnostic process owing to its innate traits of irreversibility with subtle and gradual progression. These characteristics make AD biomarker identification from structural brain imaging (e.g., structural MRI) scans quite challenging. Furthermore, there is a high possibility of getting entangled with normal aging. We propose a novel deep-learning approach through eXplainable AD Likelihood Map Estimation (XADLiME) for AD progression modeling over 3D sMRIs using clinically-guided prototype learning. Specifically, we establish a set of top…
Purpose Predicting the progression of MCI to Alzheimer's disease is an important step in reducing the progression of the disease. Therefore, many methods have been introduced for this task based on deep learning. Among these approaches, the methods based on ROIs are in a good position in terms of accuracy and complexity. In these techniques, some specific parts of the brain are extracted as ROI manually for all of the patients. Extracting ROI manually is time-consuming and its results depend on human expertness and precision. Method To overcome these limitations, we propose a novel smart metho…
In this work, we propose three explainable deep learning architectures to automatically detect patients with Alzheimer`s disease based on their language abilities. The architectures use: (1) only the part-of-speech features; (2) only language embedding features and (3) both of these feature classes via a unified architecture. We use self-attention mechanisms and interpretable 1-dimensional ConvolutionalNeural Network (CNN) to generate two types of explanations of the model`s action: intra-class explanation and inter-class explanation. The inter-class explanation captures the relative importanc…
Deep Learning Approaches with Explainable AI for Differentiating Alzheimer Disease and Mild Cognitive Impairment
School of Mathematical and Natural Sciences（数学与自然科学学院）；Arizona State University（亚利桑那州立大学）；Department of Mathematics（数学系）；Florida Gulf Coast University（佛罗里达海岸大学）；Department of Public Health（公共卫生系）；Julia Jones Matthews School of Population and Public Health（朱莉亚·琼斯·马修斯人口与公共卫生学院）；Texas Tech University Health Sciences Center（德克萨斯农工大学健康科学中心）
Instituto Superior Técnico, Universidade de Lisboa（里斯本大学高等技术学院）
Most machine learning classifiers give predictions for new examples accurately, yet without indicating how trustworthy predictions are. In the medical domain, this hampers their integration in decision support systems, which could be useful in the clinical practice. We use a supervised learning approach that combines Ensemble learning with Conformal Predictors to predict conversion from Mild Cognitive Impairment to Alzheimer's Disease. Our goal is to enhance the classification performance (Ensemble learning) and complement each prediction with a measure of credibility (Conformal Predictors). O…
Flexible and Explainable Graph Analysis for EEG-based Alzheimer's Disease Classification
Stevens Institute of Technology（史蒂文斯理工学院）；Semcer Center for Healthcare Innovation（塞姆瑟医疗创新中心）；New Jersey Institute of Technology（新泽西理工学院）；Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences（中国科学院深圳先进技术研究院）；Institute of Brain Science, College of Medicine, National Yang-Ming Chiao Tung University（国立阳明交通大学医学院脑科学研究所）
The Charles Stark Draper Laboratory, Inc.（查尔斯·斯塔克·德雷珀实验室有限公司）
Accurate diagnosis of Alzheimer's Disease (AD) entails clinical evaluation of multiple cognition metrics and biomarkers. Metrics such as the Alzheimer's Disease Assessment Scale - Cognitive test (ADAS-cog) comprise multiple subscores that quantify different aspects of a patient's cognitive state such as learning, memory, and language production/comprehension. Although computer-aided diagnostic techniques for classification of a patient's current disease state exist, they provide little insight into the relationship between changes in brain structure and different aspects of a patient's cogniti…
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer's disease diagnosis
Faculty of Electrical and Computer Engineering, National Technical University of Athens（雅典国立技术大学电气与计算机工程学院）；Center for Biomedical Image Computing and Analytics, University of Pennsylvania（宾夕法尼亚大学生物医学图像计算与分析中心）
The Ohio State University（俄亥俄州立大学）；University of Pennsylvania（宾夕法尼亚大学）；Indiana University（印第安纳大学）
Background:Cognitive assessments represent the most common clinical routine for the diagnosis of Alzheimer's Disease (AD). Given a large number of cognitive assessment tools and time-limited office visits, it is important to determine a proper set of cognitive tests for different subjects. Most current studies create guidelines of cognitive test selection for a targeted population, but they are not customized for each individual subject. In this manuscript, we develop a machine learning paradigm enabling personalized cognitive assessments prioritization. Method: We adapt a newly developed lear…
Linguistic and Audio Embedding-Based Machine Learning for Alzheimer's Dementia and Mild Cognitive Impairment Detection: Insights from the PROCESS Challenge
Singapore University of Technology and Design（新加坡科技设计大学）；Hochschule für Musik Detmold（音乐学院Detmold）
Alzheimer's Disease (AD) is a neurodegenerative disease that affects subjects in a broad range of severity and is assessed in clinical trials with multiple cognitive and functional instruments. As clinical trials in AD increasingly focus on earlier stages of the disease, especially Mild Cognitive Impairment (MCI), the ability to model subject outcomes across the disease spectrum is extremely important. We use unsupervised machine learning models called Conditional Restricted Boltzmann Machines (CRBMs) to create Digital Twins of AD subjects. Digital Twins are simulated clinical records that sha…
Platform of Bioinformatics (PBI), University Lausanne Hospital (CHUV) and Faculty of Biology and Medicine (FBM), University of Lausanne (UNIL)（生物信息平台（PBI）、日内瓦大学医院（CHUV）和生物医学学院（FBM）、日内瓦大学（UNIL））；PBI, CHUV and FBM, UNIL（PBI、CHUV和FBM、UNIL）；Department of Biomedical Engineering, University of Basel（生物医学工程系、巴塞尔大学）；Roche Diagnostics International Ltd.（罗氏诊断国际有限公司）；F. Hoffmann - La Roche Ltd., Roche Information Solutions（F. Hoffmann-La Roche有限公司、罗氏信息解决方案）
Isik University（伊希克大学）；Istanbul Technical University（伊斯坦布尔技术大学）
Retinal optical coherence tomography (OCT) and optical coherence tomography angiography (OCTA) are promising tools for the (early) diagnosis of Alzheimer's disease (AD). These non-invasive imaging techniques are cost-effective and more accessible than alternative neuroimaging tools. However, interpreting and classifying multi-slice scans produced by OCT devices is time-consuming and challenging even for trained practitioners. There are surveys on machine learning and deep learning approaches concerning the automated analysis of OCT scans for various diseases such as glaucoma. However, the curr…
Queen Mary University of London（伦敦玛丽女王大学）；Jožef Stefan Institute（约热夫·斯特凡研究所）
This paper is a submission to the Alzheimer's Dementia Recognition through Spontaneous Speech (ADReSS) challenge, which aims to develop methods that can assist in the automated prediction of severity of Alzheimer's Disease from speech data. We focus on acoustic and natural language features for cognitive impairment detection in spontaneous speech in the context of Alzheimer's Disease Diagnosis and the mini-mental state examination (MMSE) score prediction. We proposed a model that obtains unimodal decisions from different LSTMs, one for each modality of text and audio, and then combines them us…
Deep Convolutional Neural Networks (CNNs) are becoming prominent models for semi-automated diagnosis of Alzheimer's Disease (AD) using brain Magnetic Resonance Imaging (MRI). Although being highly accurate, deep CNN models lack transparency and interpretability, precluding adequate clinical reasoning and not complying with most current regulatory demands. One popular choice for explaining deep image models is occluding regions of the image to isolate their influence on the prediction. However, existing methods for occluding patches of brain scans generate images outside the distribution to whi…
University of Science and Technology of China（中国科学技术大学）；The Ohio State University（俄亥俄州立大学）
Early and accurate diagnosis of Alzheimer's disease (AD) and its prodromal period mild cognitive impairment (MCI) is essential for the delayed disease progression and the improved quality of patients'life. The emerging computer-aided diagnostic methods that combine deep learning with structural magnetic resonance imaging (sMRI) have achieved encouraging results, but some of them are limit of issues such as data leakage and unexplainable diagnosis. In this research, we propose a novel end-to-end deep learning approach for automated diagnosis of AD and localization of important brain regions rel…
An Explainable Transformer Model for Alzheimer's Disease Detection Using Retinal Imaging
Nanotechnology, Biotechnology, Information Technology and Cognitive Science Laboratory, University of Tehran（特拉布宗大学纳米技术、生物技术、信息技术和认知科学实验室）；Department of Mechatronics, School of Intelligent Systems, College of Interdisciplinary Science and Technology, University of Tehran（特拉布宗大学机电系，智能系统学院，跨学科科学与技术学院）；School of Science and Technology, Faculty of Science, Agriculture, Business and Law, University of New England（新英格兰大学科学与技术学院，科学、农业、商业与法律学院）；Graduate School of Health, University of Technology Sydney（悉尼技术大学健康研究生院）；School of Medicine and Public Health, College of Health, Medicine and Wellbeing, The University of Newcastle（新castle大学医学与公共卫生学院，健康、医学与福祉学院）
Multi-Scale Graph Theoretical Analysis of Resting-State fMRI for Classification of Alzheimer's Disease, Mild Cognitive Impairment, and Healthy Controls
University of Bojnord（博季努尔德大学）；Munster Technological University（芒斯特理工大学）
CogniAlign: Word-Level Multimodal Speech Alignment with Gated Cross-Attention for Alzheimer's Detection
Department of Computer Science and Technology, University of Alicante, Alicante, Spain（计算机科学与技术系，阿利坎特大学，阿利坎特，西班牙）；Valencian Graduate School and Research Network of Artificial Intelligence, Valencia, Spain（瓦伦西亚人工智能研究生学校与研究网络，瓦伦西亚，西班牙）；Department of Software and Computing Systems, University of Alicante, Alicante, Spain（软件与计算系统系，阿利坎特大学，阿利坎特，西班牙）
Syracuse University（雪城大学）；SUNY Upstate Medical University（纽约州立大学北部医科大学）
Alzheimer's disease is one of the diseases that mostly affects older people without being a part of aging. The most common symptoms include problems with communicating and abstract thinking, as well as disorientation. It is important to detect Alzheimer's disease in early stages so that cognitive functioning would be improved by medication and training. In this paper, we propose two attention model networks for detecting Alzheimer's disease from MRI images to help early detection efforts at the preclinical stage. We also compare the performance of these two attention network models with a base…
sMRI-PatchNet: A novel explainable patch-based deep learning network for Alzheimer's disease diagnosis and discriminative atrophy localisation with Structural MRI
Manchester Metropolitan University（曼彻斯特城市大学）；Brunel University（布鲁内尔大学）；University of Sheffield（谢菲尔德大学）；Nanjing University of Aeronautics and Astronautics（南京航空航天大学）
University of Sheffield（谢菲尔德大学）；Royal Hallamshire Hospital（皇家哈勒姆医院）；Sheffield Institute for Translational Neuroscience（谢菲尔德转化神经科学研究所）
Cognitive decline is a sign of Alzheimer's disease (AD), and there is evidence that tracking a person's eye movement, using eye tracking devices, can be used for the automatic identification of early signs of cognitive decline. However, such devices are expensive and may not be easy-to-use for people with cognitive problems. In this paper, we present a new way of capturing similar visual features, by using the speech of people describing the Cookie Theft picture - a common cognitive testing task - to identify regions in the picture prompt that will have caught the speaker's attention and elici…
Faculty of Science, Northern Border University（北部边境大学理学院）；SEU Saudi Electronic University（沙特电子大学）
Alzheimer's disease is a neurogenerative disease that alters memories, cognitive functions leading to death. Early diagnosis of the disease, by detection of the preliminary stage, called Mild Cognitive Impairment (MCI), remains a challenging issue. In this respect, we introduce, in this paper, a powerful classification architecture that implements the pre-trained network AlexNet to automatically extract the most prominent features from Magnetic Resonance Imaging (MRI) images in order to detect the Alzheimer's disease at the MCI stage. The proposed method is evaluated using a big database from …
DeepTokenEEG Enhancing Mild Cognitive Impairment and Alzheimers Classification via Tokenized EEG Features
Hanoi University of Science and Technology（河内科学与技术大学）；VNU University of Engineering and Technology（越南工程与技术大学）；Central Military Hospital 108（第108中心军医院）；University of California, Irvine（加州大学尔湾分校）
Most approaches to machine learning from electronic health data can only predict a single endpoint. Here, we present an alternative that uses unsupervised deep learning to simulate detailed patient trajectories. We use data comprising 18-month trajectories of 44 clinical variables from 1908 patients with Mild Cognitive Impairment or Alzheimer's Disease to train a model for personalized forecasting of disease progression. We simulate synthetic patient data including the evolution of each sub-component of cognitive exams, laboratory tests, and their associations with baseline clinical characteri…
A Fine Evaluation Method for Cube Copying Test for Early Detection of Alzheimer's Disease
Anhui Jianzhu University（安徽建筑大学）；Hefei Smart Healthcare Center & Apartment for Elderly, Hefei Industrial Investment Co., LTD（合肥产投集团合肥智慧养老中心暨老年公寓）；Changhai Hospital, Second Military Medical University（第二军医大学长海医院）
Kharkevich Institute for Information Transmission Problems（哈尔克维奇信息传输问题研究所）；Skolkovo Institute of Science and Technology（斯科尔科沃科学技术研究所）
Nowadays, a lot of scientific efforts are concentrated on the diagnosis of Alzheimer's Disease (AD) applying deep learning methods to neuroimaging data. Even for 2017, there were published more than a hundred papers dedicated to AD diagnosis, whereas only a few works considered a problem of mild cognitive impairments (MCI) conversion to the AD. However, the conversion prediction is an important problem since approximately 15% of patients with MCI converges to the AD every year. In the current work, we are focusing on the conversion prediction using brain Magnetic Resonance Imaging and clinical…
Northwestern University（西北大学）；University of Florida（佛罗里达大学）；Weill Cornell Medicine（威尔康奈尔医学院）；Mayo Clinic（梅奥诊所）
Objective: We develop a deep learning framework based on the pre-trained Bidirectional Encoder Representations from Transformers (BERT) model using unstructured clinical notes from electronic health records (EHRs) to predict the risk of disease progression from Mild Cognitive Impairment (MCI) to Alzheimer's Disease (AD). Materials and Methods: We identified 3657 patients diagnosed with MCI together with their progress notes from Northwestern Medicine Enterprise Data Warehouse (NMEDW) between 2000-2020. The progress notes no later than the first MCI diagnosis were used for the prediction. We fi…
Longitudinal Progression Prediction of Alzheimer's Disease with Tabular Foundation Model
Department of Computer Science Emory University Atlanta GA USA（埃默里大学计算机科学系）；Center for Data Science Nell Hodgson Woodruff School of Nursing Emory University Atlanta GA USA（埃默里大学数据科学中心）；Department of Computer Science Georgia State University Atlanta GA USA（佐治亚州立大学计算机科学系）；Tri-Institutional Center for Translational Research in Neuroimaging and Data Science (TReNDS) Atlanta GA USA（神经影像与数据科学转化研究三机构中心（TReNDS））
Radiogenomic Bipartite Graph Representation Learning for Alzheimer's Disease Detection
Department of Electrical and Computer Engineering, Ohio State University（电子与计算机工程系，俄亥俄州立大学）；Department of Computer Science and Engineering, Ohio State University（计算机科学与工程系，俄亥俄州立大学）
Ludwig-Maximilians-Universität（路德维希-马克西米利安大学）；Indiana University School of Medicine（印第安纳大学医学院）
The current state-of-the-art deep neural networks (DNNs) for Alzheimer's Disease diagnosis use different biomarker combinations to classify patients, but do not allow extracting knowledge about the interactions of biomarkers. However, to improve our understanding of the disease, it is paramount to extract such knowledge from the learned model. In this paper, we propose a Deep Factorization Machine model that combines the ability of DNNs to learn complex relationships and the ease of interpretability of a linear model. The proposed model has three parts: (i) an embedding layer to deal with spar…
Explainable concept mappings of MRI: Revealing the mechanisms underlying deep learning-based brain disease classification
Medical University of Graz（格拉茨医科大学）；Monash University（莫纳什大学）；Fraunhofer Heinrich Hertz Institute（弗劳恩霍夫海因里希·赫兹研究所）；Technische Universität Berlin（柏林工业大学）；BIFOLD – Berlin Institute for the Foundations of Learning and Data（BIFOLD——柏林学习与数据基础研究所）
Flexible Multimodal Neuroimaging Fusion for Alzheimer's Disease Progression Prediction
Department of Computer Science and Engineering（计算机科学与工程系）；Department of Biomedical Informatics（生物医学信息学系）；Department of Neurology（神经病学系）；Translational Data Analytics Institute（转化数据分析研究所）
