Summary of A Comprehensive Interpretable Machine Learning Framework For Mild Cognitive Impairment and Alzheimer’s Disease Diagnosis, by Maria Eleftheria Vlontzou et al.
A comprehensive interpretable machine learning framework for Mild Cognitive Impairment and Alzheimer’s disease diagnosis
by Maria Eleftheria Vlontzou, Maria Athanasiou, Kalliopi Dalakleidi, Ioanna Skampardoni, Christos Davatzikos, Konstantina Nikita
First submitted to arxiv on: 12 Dec 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The proposed interpretable machine learning framework aims to improve Mild Cognitive Impairment (MCI) and Alzheimer’s disease (AD) diagnosis by developing robust ML models with reliable interpretations. The study leverages ensemble learning, attribution-based methods, and counterfactual explanations to generate diverse insights related to the pathophysiology of MCI/AD. A unification method combining SHAP with counterfactual explanations assesses the interpretability techniques’ robustness. The best-performing model achieves 87.5% balanced accuracy and 90.8% F1-score, highlighting significant volumetric and genetic features relevant to MCI/AD risk. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper helps doctors better diagnose Alzheimer’s disease by using special computer models that can explain their decisions. The researchers created a new way to make sure the models are correct and reliable. They used data from brain scans and genetic tests to train the models, which were then tested on different types of patients. The best model was able to correctly diagnose 87.5% of patients, and it also highlighted important features that doctors can use to identify people at risk. |
Keywords
» Artificial intelligence » F1 score » Machine learning
