ArticleScientific reports2025
A comprehensive interpretable machine learning framework for mild cognitive impairment and Alzheimer's disease diagnosis.
Article in Scientific reports, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers, 1 of them a synthesis that pooled it.
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Who cites it
13 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 novel diagnosis method based on multimodal-aware bi-objective competitive mechanism for Alzheimer's disease.Scientific reports · 2026Article
- FUSION-AD: interpretable AI framework for risk assessment and subgroup discovery in Alzheimer's disease.Frontiers in neuroinformatics · 2026Article
- Development of Alzheimer's disease risk score for future integrated primary care: a white-box approach.Frontiers in aging neuroscience · 2026Article
- Baseline indices of cardiorespiratory fitness for predicting acute mountain sickness: an interpretable four-task ensemble model.Frontiers in public health · 2026Article
- Development of an explainable machine learning model for Alzheimer's disease prediction using clinical and behavioural features.MethodsX · 2025Article
- The ICN-UN Battery: A Machine Learning-Optimized Tool for Expeditious Alzheimer's Disease Diagnosis.Diagnostics (Basel, Switzerland) · 2025Article
- Early classification of functional connectomes in Parkinson's disease: a comparison of machine learning classifiers using multi-scale topological features.BMC medical informatics and decision making · 2025Article
- Development of Alzheimer's Disease Risk Score for Future Primary Care: A White-Box Approach.medRxiv : the preprint server for health sciences · 2025Article
- Cerium-based nanoparticles for neurodegeneration: emerging redox therapeutics beyond pharmaceuticals.RSC advances · 2025Review
- Gallstone Classification Using Random Forest Optimized by Sand Cat Swarm Optimization Algorithm with SHAP and DiCE-Based Interpretability.Sensors (Basel, Switzerland) · 2025Article
- Regularized regression outperforms trees for predicting cognitive function in the Health and Retirement Study.Machine learning with applications · 2025Article
- Comparative assessment of fairness definitions and bias mitigation strategies in machine learning-based diagnosis of Alzheimer's disease from MR images.Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference · 2025Article
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6 authors.
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Abstract
An interpretable machine learning (ML) framework is introduced to enhance the diagnosis of Mild Cognitive Impairment (MCI) and Alzheimer's disease (AD) by ensuring robustness of the ML models' interpretations. The dataset used comprises volumetric measurements from brain MRI and genetic data from healthy individuals and patients with MCI/AD, obtained through the Alzheimer's Disease Neuroimaging Initiative. The existing class imbalance is addressed by an ensemble learning approach, while various attribution-based and counterfactual-based interpretability methods are leveraged towards producing diverse explanations related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques' robustness. The best performing model yielded 87.5% balanced accuracy and 90.8% F1-score. The attribution-based interpretability methods highlighted significant volumetric and genetic features related to MCI/AD risk. The unification method provided useful insights regarding those features' necessity and sufficiency, further showcasing their significance in MCI/AD diagnosis.
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