1. Machine Learning Techniques for the Evaluating of External Skeletal Fixation Structure
Dr.Khaled Rasheed
Dr. Walter D. Potter
Dr. Dennis N. Aron
Ning Suo

2. Presentation Outline Introduction --- Background --- Related Works
Prior Analysis
Methods proposed
Experiment and Result
Future Directions
Questions & Suggestions

3. Introduction Fact Related work * Accident * External fixation
* 136,437,480 pets
* Accident
* External fixation
* What we can help
Related work
* "Bone-FixES"
* Decision Tree

4. Prior Analysis Data 12 patients; 5 treatment proposal; 35 parameters
Methods
---Full parameters approach vs. Reduced Parameters
* Full parameters: 35 parameters
* Reduced parameters: 4 parameters
---Binary Prediction vs. Multiple Class Prediction
* Binary prediction : Preprocessing data
i.e. 1,2,3  0
* Multiple prediction: Score this treatment

5. Proposed Methods ? Genetic Algorithm Set ANN Structure Decision Tree
Classifier System
Artificial Neural Network
Genetic Algorithm Set ANN Structure

6. Methods I--Decision Tree
What is it?
* Inductive learning, positive and negative examples
* If then rules
* Heuristic method
Featured operators
* Cross-validation, boosting, pruning, winnowing attributes
Program C5.0 by Ross Quinlan
i.e. if KE ^ (pin_num>4) ^ (Duration_surgery<3) ^ (close)^ (frame_num>5) then score 8

7. Method II-Classifier System
What is it?
* Machine learning system
* Learn rules and guide its performance
Components

8. Method III- ANN Three Layer backpropgation Neural Network
Momentum and Learning Rate
Spread

9. Method IV-G.A.A.N.N. Generational GA Representation Selection Method
Genetic Operator
Fitness
Stop Criteria

10. Experiment Result 1. Decision Tree: Correct Rate=66.66%
Full Data with Multi-class Prediction
Reduced Data with Multi-class Prediction
Correct Rate=66.66%
Correct Rate=73.33%

11. Experiment Result (con’d)
2. Classifier System
Classifier System
Full Data with Multi-class Prediction
Reduced Data with Multi-class Prediction
Correct Rate=21%
Correct Rate=44%

12. Experiment Result (con’d)
3. Artificial Neural Network
Artificial Neural Network
Full Data with Multi-class Prediction
Reduced Data with Multi-class Prediction
Correct Rate=31.7%
Correct Rate=45%

13. Experiment Result (con’d)
4. GAANN
GAANN
Full Data with Multi-class Prediction
Reduced Data with Multi-class Prediction
Correct Rate=63.3%
Correct Rate=53.3%

14. Experiment Result (con’d)
Decision Tree
Classifier System
GAANN
Artificial Neural Network
GAANN

15. Conclusion
Decision tree did very well since boosting and cross validation techniques were applied.
GAANN shows more potential. A GA with more features such as special operators will performs better.
Data set too small.

16. Note Special Thanks: FPAS Web Page Comments Dr. Ron McClendon
Marc Schenkel
Jaymin Kessler
Jason Schlachter
FPAS Web Page
Comments