Machine Learning Techniques for the Evaluating Machine Learning Techniques for the Evaluating of External Skeletal Fixation Structureof External Skeletal Fixation Structure

Dr.Khaled Rasheed

Dr. Walter D. Potter

Dr. Dennis N. Aron

Ning Suo

Introduction --- Background

--- Related Works Prior Analysis Methods proposed Experiment and Result Future Directions Questions & Suggestions

Presentation OutlinePresentation Outline

IntroductionIntroduction

Fact* 136,437,480 pets* Accident* External fixation* What we can help

Related work * "Bone-FixES" * Decision Tree

Prior AnalysisPrior 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 datai.e. 1,2,3 0* Multiple prediction: Score this treatment

Proposed MethodsProposed Methods

?

Artificial Neural Network

Decision Tree

Classifier System

Genetic Algorithm Set ANN Structure

Methods I--Decision TreeMethods 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

Method II-Classifier SystemMethod II-Classifier System

What is it?

* Machine learning system * Learn rules and guide its performance

Components

Method III- ANNMethod III- ANN

Three Layer backpropgation Neural Network Momentum and Learning Rate Spread

Method IV-G.A.A.N.N.Method IV-G.A.A.N.N.

Generational GA Representation Selection Method Genetic Operator Fitness Stop Criteria

Experiment ResultExperiment Result1. Decision Tree:

Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction

Correct Rate=73.33% Correct Rate=66.66%

Experiment Result (con’d)Experiment Result (con’d)2. Classifier System

Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction

Correct Rate=21% Correct Rate=44%

Experiment Result (con’d)Experiment Result (con’d)3. Artificial Neural Network

Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction

Correct Rate=31.7% Correct Rate=45%

Experiment Result (con’d)Experiment Result (con’d)4. GAANN

Full Data with Multi-class Prediction Reduced Data with Multi-class Prediction

Correct Rate=63.3% Correct Rate=53.3%

Experiment Result (con’d)Experiment Result (con’d)Decision Tree Classifier System

Artificial Neural Network GAANN

ConclusionConclusion

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.

NoteNote

Special Thanks: Dr. Ron McClendon Marc Schenkel Jaymin Kessler Jason Schlachter

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