learnad-alzheimer-interpretable-rules

---
name: learnad-alzheimer-interpretable-rules
description: "LearnAD: neuro-symbolic method for Alzheimer's disease classification from brain MRI, learning fully interpretable rules combining statistical models and decision logic. Activation: LearnAD, Alzheimer interpretable rules, neuro-symbolic AD classification."
---

# LearnAD: Interpretable Rules for Alzheimer's Disease Classification

> Neuro-symbolic method for predicting Alzheimer's disease from brain MRI data, learning fully interpretable rules that combine statistical modeling with decision logic for transparent clinical decision-making.

## Metadata
- **Source**: arXiv:2601.00877
- **Authors**: Thomas Andrews, Mark Law, Sara Ahmadi-Abhari
- **Published**: 2026-01

## Core Methodology

### Key Innovation
Combines statistical feature extraction from brain MRI with neuro-symbolic rule learning to produce fully interpretable classification rules for Alzheimer's disease, achieving transparency without sacrificing predictive accuracy.

### Technical Framework
1. **Statistical Feature Extraction**: Extract quantitative features from brain MRI (hippocampal volume, cortical thickness, etc.)
2. **Decision Logic Layer**: Apply symbolic reasoning over extracted features
3. **Rule Learning**: Automatically discover interpretable if-then rules that classify AD vs. controls
4. **Hybrid Architecture**: Statistical models capture continuous patterns, symbolic rules provide interpretable boundaries
5. **Validation**: Test rule quality on held-out data, compare with black-box methods

### Why Neuro-Symbolic for AD
- Clinical decisions require interpretable reasoning
- Black-box models cannot explain why a patient is classified as AD
- Rules can be validated against known clinical biomarkers
- Enables discovery of novel biomarker combinations

## Implementation Guide

### Prerequisites
- Brain MRI dataset with AD/control labels
- Neuroimaging feature extraction tools (FreeSurfer, FSL)
- Rule learning framework (Inductive Logic Programming or similar)

### Step-by-Step
1. Preprocess MRI data (segmentation, normalization)
2. Extract quantitative features (volumes, thickness, connectivity)
3. Define background knowledge (known AD biomarkers, anatomical relationships)
4. Apply rule learning algorithm to discover classification rules
5. Validate rules: accuracy, sensitivity, specificity, interpretability
6. Deploy as clinical decision support tool

### Code Example
```python
class LearnAD:
    def __init__(self):
        self.statistical_models = {}
        self.rules = []
    
    def extract_features(self, mri_data):
        """Extract statistical features from MRI."""
        features = {
            'hippocampal_volume': compute_volume(mri_data, 'hippocampus'),
            'cortical_thickness': compute_thickness(mri_data),
            'ventricle_ratio': compute_ventricle_ratio(mri_data),
        }
        return features
    
    def learn_rules(self, features, labels):
        """Learn interpretable classification rules."""
        rules = [
            "IF hippocampal_volume < threshold1 AND cortical_thickness < threshold2 THEN AD",
            "IF ventricle_ratio > threshold3 THEN AD",
        ]
        return rules
```

## Applications
- Clinical decision support for Alzheimer's diagnosis
- Biomarker discovery through rule analysis
- Transparent AI for healthcare applications
- Medical education and training

## Pitfalls
- Rule complexity may limit interpretability if too many conditions
- Requires high-quality MRI data with consistent preprocessing
- May not capture subtle patterns that deep learning methods detect
- Generalization across different scanner types and protocols

## Related Skills
- alzheimer-pet-suvr-network-models
- higher-order-topological-ad-alzheimer
- neuroaps-net-alzheimer-point-cloud