Project 4: The Medical Diagnostic Expert System (Applying the Engines)

Build an expert system that uses rules to diagnose conditions from symptoms.

Quick Reference

Attribute Value
Difficulty Level 4: Expert
Time Estimate 16-24 hours
Language Python
Prerequisites Projects 2-3
Key Topics expert systems, rule bases, inference traces

1. Learning Objectives

By completing this project, you will:

  1. Build a medical rule base for diagnoses.
  2. Apply forward/backward chaining for inference.
  3. Explain diagnostic conclusions with traces.
  4. Handle conflicting rules with priorities.
  5. Evaluate accuracy on test cases.

2. Theoretical Foundation

2.1 Expert System Reasoning

Expert systems apply symbolic rules to infer conclusions and must explain reasoning clearly.

3. Project Specification

3.1 What You Will Build

A diagnostic system that takes symptom facts and returns likely conditions with explanations.

3.2 Functional Requirements

  1. Medical rule base with conditions and symptoms.
  2. Inference engine (forward/backward).
  3. Explanation module for reasoning traces.
  4. Conflict resolution between rules.
  5. Evaluation on sample cases.

3.3 Non-Functional Requirements

  • Deterministic inference.
  • Clear explanations for decisions.
  • Configurable rule priorities.

4. Solution Architecture

4.1 Components

Component Responsibility
Rule Base Store medical rules
Inference Engine Apply rules
Explainer Generate reasoning trace
Evaluator Compare diagnoses

5. Implementation Guide

5.1 Project Structure

SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY/P04-medical-expert/
├── src/
│   ├── rules.py
│   ├── engine.py
│   ├── explain.py
│   └── eval.py

5.2 Implementation Phases

Phase 1: Rule base (6-8h)

  • Define symptoms and diagnoses.
  • Checkpoint: rules load correctly.

Phase 2: Inference (6-8h)

  • Apply chaining engine.
  • Checkpoint: diagnoses returned.

Phase 3: Explanation + eval (4-8h)

  • Add reasoning traces.
  • Evaluate against test cases.
  • Checkpoint: accuracy report created.

6. Testing Strategy

6.1 Test Categories

Category Purpose Examples
Unit rules correct rule parsing
Integration inference diagnosis output
Regression evaluation consistent accuracy

6.2 Critical Test Cases

  1. Conflicting symptoms resolved with priority.
  2. Explanation shows rule path.
  3. Diagnoses match test set.

7. Common Pitfalls & Debugging

Pitfall Symptom Fix
Rule conflicts wrong diagnosis add priorities
Missing explanations black box trace rule firing
Overfitting rules poor coverage expand rule set

8. Extensions & Challenges

Beginner

  • Add more symptom categories.
  • Add CLI input.

Intermediate

  • Add confidence scores.
  • Add case-based reasoning.

Advanced

  • Add probabilistic rules.
  • Add real-world dataset evaluation.

9. Real-World Connections

  • Clinical decision support uses expert systems.
  • Regulatory environments require explainability.

10. Resources

  • Expert systems textbooks
  • Medical knowledge base examples

11. Self-Assessment Checklist

  • I can build an expert rule base.
  • I can explain diagnostic reasoning.
  • I can evaluate diagnostic accuracy.

12. Submission / Completion Criteria

Minimum Completion:

  • Diagnostic expert system

Full Completion:

  • Explanations + evaluation

Excellence:

  • Probabilistic rules
  • Real dataset testing

This guide was generated from project_based_ideas/AI_AGENTS_LLM_RAG/SYMBOLIC_AI_AND_EXPERT_SYSTEMS_MASTERY.md.