1. Medical Diagnosis via Genetic Programming
AI Project #2
Biointelligence lab
Cho, Dong-Yeon

2. © 2006 SNU CSE Biointelligence Lab
Project Purpose
Medical Diagnosis
To predict the presence or absence of a disease given the results of various medical tests carried out on a patient
Human experts (M.D.) vs Machine (GP)
Two Data Sets
Heart Disease
Diabetes
© 2006 SNU CSE Biointelligence Lab

3. © 2006 SNU CSE Biointelligence Lab
Heart Disease
Data Description
Number of patients (270)
Absence (150)
Presence (120)
13 attributes
age
sex
chest pain type (4 values)
resting blood pressure
serum cholestoral in mg/dl
fasting blood sugar > 120 mg/dl
resting electrocardiographic results (values 0,1,2)
maximum heart rate achieved
exercise induced angina
oldpeak = ST depression induced by exercise relative to rest
the slope of the peak exercise ST segment
number of major vessels (0-3) colored by flourosopy
thal: 3 = normal; 6 = fixed defect; 7 = reversable defect
© 2006 SNU CSE Biointelligence Lab

4. © 2006 SNU CSE Biointelligence Lab
Learning a Classifier
GP settings
Functions
Numerical and condition operators
{+, -, *, /, exp, log, sin, cos, sqrt, iflte ifltz, …}
Some operators should be protected from the illegal operation.
Terminals
Input attributes and constants
{x1, x2, … x13, R} where R  [a, b]
Additional parameters
Threshold value
For preprocessing (normalization)
© 2006 SNU CSE Biointelligence Lab

5. © 2006 SNU CSE Biointelligence Lab
Cross Validation (1/3)
K-fold Cross Validation
The data set is randomly divided into k subsets.
One of the k subsets is used as the test set and the other k-1 subsets are put together to form a training set. D1 D2 D3 D4 D5 D6 45 45 45 45 45 45 D1 D2 D3 D4 D6 D5 45 45 45 45 45 45 D2 D3 D4 D5 D6 D1 45 45 45 45 45 45
© 2006 SNU CSE Biointelligence Lab

6. © 2006 SNU CSE Biointelligence Lab
Cross Validation (2/3)
Confusion Matrix for test data sets
Number of patients = p + q + r + s
Accuracy
True
Predict
Positive
Negative p q r s
© 2006 SNU CSE Biointelligence Lab

7. © 2006 SNU CSE Biointelligence Lab
Cross Validation (3/3)
Cross validation and Confusion Matrix
At least 10 runs for your k value.
Show the confusion matrix for the best result of your experiments.
Run
Accuracy 1 2
 10
Average
© 2006 SNU CSE Biointelligence Lab

8. © 2006 SNU CSE Biointelligence Lab
Initialization
Maximum initial depth of trees Dmax is set.
Full method (each branch has depth = Dmax):
nodes at depth d < Dmax randomly chosen from function set F
nodes at depth d = Dmax randomly chosen from terminal set T
Grow method (each branch has depth  Dmax):
nodes at depth d < Dmax randomly chosen from F  T
nodes at depth d = Dmax randomly chosen from T
Common GP initialisation: ramped half-and-half, where grow and full method each deliver half of initial population
© 2006 SNU CSE Biointelligence Lab

9. © 2006 SNU CSE Biointelligence Lab
Fitness Function
Maximization problem
Number of the correctly classified patients
Minimization problem
Number of the incorrectly classified patients
Mean Squared Error
N: number of training data
© 2006 SNU CSE Biointelligence Lab

10. © 2006 SNU CSE Biointelligence Lab
Selection (1/2)
Fitness proportional (roulette wheel) selection
The roulette wheel can be constructed as follows.
Calculate the total fitness for the population.
Calculate selection probability pk for each chromosome vk.
Calculate cumulative probability qk for each chromosome vk.
© 2006 SNU CSE Biointelligence Lab

11. © 2006 SNU CSE Biointelligence Lab
Procedure: Proportional_Selection
Generate a random number r from the range [0,1].
If r  q1, then select the first chromosome v1; else, select the kth chromosome vk (2 k  pop_size) such that qk-1 < r  qk. pk qk 1 2 3 4 5 6 7 8 9 10
© 2006 SNU CSE Biointelligence Lab

12. © 2006 SNU CSE Biointelligence Lab
Selection (2/2)
Tournament selection
Tournament size q
Ranking-based selection
2    POP_SIZE
1  +  2 and - = 2 - +
© 2006 SNU CSE Biointelligence Lab

13. © 2006 SNU CSE Biointelligence Lab
GP Flowchart
GA loop
GP loop
© 2006 SNU CSE Biointelligence Lab

14. © 2006 SNU CSE Biointelligence Lab
Bloat
Bloat = “survival of the fattest”, i.e., the tree sizes in the population are increasing over time
Ongoing research and debate about the reasons
Needs countermeasures, e.g.
Prohibiting variation operators that would deliver “too big” children
Parsimony pressure: penalty for being oversized
© 2006 SNU CSE Biointelligence Lab

15. © 2006 SNU CSE Biointelligence Lab

16. © 2006 SNU CSE Biointelligence Lab
Experiments
Two problems
Heart Disease
Pima Indian diabetes
Various experimental setup
Termination condition: maximum_generation
Various settings
Effects of the penalty term
Different function and terminal sets
Selection methods and their parameters
Crossover and mutation probabilities
© 2006 SNU CSE Biointelligence Lab

17. © 2006 SNU CSE Biointelligence Lab
Results
For each problem
Result table and your analysis
Present the optimal classifier
Draw a learning curve for the run where the best solution was found.
Compare with the results of neural networks (optional).
Different k for cross validation (optional)
Training
Test
Average  SD
Best
Worst
Setting 1
Setting 2
Setting 3
© 2006 SNU CSE Biointelligence Lab

18. © 2006 SNU CSE Biointelligence Lab
Fitness (Error)
Generation
© 2006 SNU CSE Biointelligence Lab

19. © 2006 SNU CSE Biointelligence Lab
References
Source Codes
GP libraries (C, C++, JAVA, …)
MATLAB Tool box
Web sites …
© 2006 SNU CSE Biointelligence Lab

20. © 2006 SNU CSE Biointelligence Lab
Pay Attention!
Due: May 11, 2006
Submission
Source code and executable file(s)
Proper comments in the source code
Via
Report: Hardcopy!!
Running environments and libraries (or packages) which you used.
Results for many experiments with various parameter settings
Analysis and explanation about the results in your own way
© 2006 SNU CSE Biointelligence Lab