ArticleScientific reports2024
An explainable machine learning approach for Alzheimer's disease classification.
Article in Scientific reports, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 44 papers, 1 of them a synthesis that pooled it.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
44 citing papers in PubMed, 1 synthesis or guideline pooled it.
- From Single-Modal to Multi-Modal Artificial Intelligence in Alzheimer's Disease: A Systematic Review of Databases, Modalities, Diagnostic Performance, and Clinical Translation Challenges.Sensors (Basel, Switzerland) · 2026Pooled it
- A Flexible-Horizon Clinical Decision Support Model for Dementia Early Detection and Risk Prediction in Secondary Care: A Responsible AI Approach.Journal of medical systems · 2026Article
- Weakly Supervised MRI-Based Classification of Alzheimer's Disease Using Clinical Pseudo-Labels.NeuroSci · 2026Article
- Morphometric Explanations for Deep Learning Based Neuroimaging Classifiers.Human brain mapping · 2026Article
- Decoding neuronal gene expression: integrative insights from omics and AI.Brain informatics · 2026Review
- Centralized Review of Alzheimer's Disease and Related Dementias Biomedical Repositories and Computational Methods.Bioengineering (Basel, Switzerland) · 2026Review
- A computational model to describe multi-regional brain architecture during neurodegeneration in Alzheimer's disease.Scientific reports · 2026Article
- AlzStack: Forecasting early-onset Alzheimer's with an explainable AI system using multiple data balancing techniques.Global epidemiology · 2026Article
- Amyloid-beta statuses prediction with free water MR imaging features in Alzheimer's disease using machine learning models.BMC medical imaging · 2026Article
- ChronicDPipredictor: an interpretable deep learning framework for chemical chronic and subchronic toxicity assessment.Molecular diversity · 2026Article
- A comprehensive review of explainable artificial intelligence in healthcare methods, evaluation, and clinical integration.iScience · 2026Review
- Explainable artificial intelligence for early Alzheimer's diagnosis using enhanced grey relational features and multimodal data.Scientific reports · 2026Article
- Interpretable integration of unpaired multi-omics for Alzheimer's diagnosis via cross-modal transformer reconstruction.PLoS computational biology · 2026Article
- Disease classification via interpretable machine learning based on multi-center routine coagulation test.Frontiers in molecular biosciences · 2026Article
- PredatorNet: a clinically trustworthy deep learning framework for multi-class Alzheimer's disease classification using MRI.Frontiers in aging neuroscience · 2026Article
- Alzheimer's disease prediction via an explainable CNN using genetic algorithm and SHAP values.PloS one · 2026Article
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Detection and Classification of Alzheimer's Disease Using Deep and Machine Learning.Tomography (Ann Arbor, Mich.) · 2025Article
- A Hybrid Explainable AI Framework (HXAI) for Accurate and Interpretable Diagnosis of Alzheimer's Disease.Diagnostics (Basel, Switzerland) · 2025Article
- Synergistic medical genetic evolutionary optimization and deep convolutional generative augmentation with SHAP-driven interpretability for precise Alzheimer's disease severity grading.Brain informatics · 2025Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
5 authors.
Funding
Abstract
The early diagnosis of Alzheimer's disease (AD) presents a significant challenge due to the subtle biomarker changes often overlooked. Machine learning (ML) models offer a promising tool for identifying individuals at risk of AD. However, current research tends to prioritize ML accuracy while neglecting the crucial aspect of model explainability. The diverse nature of AD data and the limited dataset size introduce additional challenges, primarily related to high dimensionality. In this study, we leveraged a dataset obtained from the National Alzheimer's Coordinating Center, comprising 169,408 records and 1024 features. After applying various steps to reduce the feature space. Notably, support vector machine (SVM) models trained on the selected features exhibited high performance when tested on an external dataset. SVM achieved a high F1 score of 98.9% for binary classification (distinguishing between NC and AD) and 90.7% for multiclass classification. Furthermore, SVM was able to predict AD progression over a 4-year period, with F1 scores reached 88% for binary task and 72.8% for multiclass task. To enhance model explainability, we employed two rule-extraction approaches: class rule mining and stable and interpretable rule set for classification model. These approaches generated human-understandable rules to assist domain experts in comprehending the key factors involved in AD development. We further validated these rules using SHAP and LIME models, underscoring the significance of factors such as MEMORY, JUDGMENT, COMMUN, and ORIENT in determining AD risk. Our experimental outcomes also shed light on the crucial role of the Clinical Dementia Rating tool in predicting AD.
Indexed as
Identifiers
What Socratic holds
Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the Socratic graph.