Breast cancer treatment is one of the medical mysteries, yet unresolved challenge for medical practitioners. The key for better treatment is early diagnosis and treatment. However, even after early diagnosis and treatment, there is high chance of recurrence. By making early prognosis, thus, patients can get better treatment. Data mining, as a knowledge mining field, can contribute on better prognosis with better accuracy rate of prediction. In this report, working on WEKA software, we are trying to show on how to get a decision tree with better accuracy rate. Dealing with the Wisconsin Breast Cancer Database, collected by Dr. William H. Wolberg, University of Wisconsin Hospitals, we will discuss on how we a decision tree data mining technique gives better prediction tool.

1. Classification of Breast Cancer dataset using
Decision Tree Induction
Abel Medhanie Gebreyesus
Sunil Nair
HINF6210 Project Presentation – November 25, 2008

2. Agenda
Objective
Dataset
Approach
Classification Methods
Decision Tree
Problems
Future direction
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3. Introduction
Breast Cancer prognosis
Breast cancer incidence is high
Improvement in diagnostic methods
Early diagnosis and treatment.
But, recurrence is high
Good prognosis is important….
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4. Objective
Significance of project
Previous work done using this dataset
Most previous work indicated room for
improvement in increasing accuracy of classifier
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5. Breast Cancer Dataset
Wisconsin Breast Cancer Database (1991) University of Wisconsin Hospitals,
Dr. William H. Wolberg
# of Instances: 699
# of Attributes: 10 plus
Class attribute
Class distribution:
Benign (2): 458 (65.5%)
Malignant (4): 241 (34.5%)
Missing Values : 16
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6. Attributes
•Indicate Cellular characteristics
•Variables are Continuous, Ordinal with 10 levels
1 id number
Sample code number
2 1-10
Clump Thickness
3 1-10
Uniformity of Cell Size
4 1-10
Uniformity of Cell Shape
5 1-10
Marginal Adhesion
6 1-10
Single Epithelial Cell Size
7 1-10
Bare Nuclei
8 1-10
Bland Chromatin
9 1-10
Normal Nucleoli
10 1-10
Mitoses
11 Class Benign (2), Malignant (4)
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7. Attributes / class - distribution
• Dataset unbalanced
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8. Our Approach
Data Pre-processing
Comparison between Classification techniques
Decision Tree Induction
Attribute Selection
J48
Evaluation
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9. Data Pre-processing
Filter out the ID column
Handle Missing Values
WEKA
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10. Data preprocessing
Two options to manage Missing data – WEKA
“Replacemissingvalues”
weka.filters.unsupervised.attribute.ReplaceMissingValues
Missing nominal and numeric attributes replaced with
mode-means
Remove (delete) the tuple with missing values.
Missing values are attribute bare nuclei = 16
Outliers
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11. Comparison chart – Handle Missing Value
Confusion Matrix
Total Correctly Classified Instances Test split = 223
Class B M Total
Accuracy Rate: PERFORMANCE EVALUATION
95.78% B 160 7 167
# Act. Exp.
M 3 63 66
DATASET RULES MAE Acc. Acc.
Total 163 70 233
Rate Rate
14 8% 94% 87%
How many predictions by chance? Complete
Missing
Expected Accuracy Rate = Kappa
11 5% 96% 90%
Statistic Removed
-is used to measure the agreement between
predicted and actual categorization of data Missing
while correcting for prediction that occurs by 14 7% 95% 89%
Replaced
chance.
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12. Data Pre-processing
Missing Value Replaced - Mean-Mode Missing Value Removed - Mean-Mode
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13. Agenda
Objective
Dataset
Approach
Data Pre-Processing
Classification Methods
Decision Tree
Problems
Future direction
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14. Classification Methods Comparison
PERFORMANCE EVALUATION
Test Set
# Act. Exp.
CLASSIFIER Total MAE Acc. Acc.
Inst. Rate Rate
Naïve Bayes
233 4% 96% 90%
Neural
233 10% 91% 79%
Network
Support Vector
233 3% 97% 94%
M
233 4% 97% 92%
DT‐J48
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15. Classification using Decision Tree
Decision Tree – WEKA J48 (C4.5)
Divide and conquer algorithm
Convert tree to Classification rules
J48 can handle numeric attributes, no need for
discretization
Attribute Selection - Information gain
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16. Attributes Selected – most IG
weka.filters.supervised.attribute.AttributeSelection-Eweka.attributeSelection.InfoGainAttributeEval-
Sweka.attributeSelection.Ranker
Rank Information Gain
Attribute
PERFORMANCE EVALUATION
1 Uniformity of Cell Size 0.675
# Act. Exp.
2 Uniformity of Cell Shape 0.66 DATASET RULES MAE Acc. Acc.
Rate Rate
3 Bare Nucleoli 0.564
Attributes
4 Bland Chromatin 0.543
11 4% 97% 92%
Selected
5 Single Epithelial Cell Size 0.505
Missing
6 Normal Nucleoli 0.466
11 5% 96% 90%
Removed
7 Clump Thickness 0.459
Missing
8 Marginal Adhesion 0.443
14 7% 95% 89%
Replaced
9 Mitosis 0.198
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17. The DT – IG/Attribute selection
Visualization
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18. Decision Tree - Problems
Concerns
Missing values
Pruning – Preprune or postprune
Estimating error rates
Unbalanced Dataset
Bias in prediction
Overfitting – in test set
Underfitting
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19. Confusion Matrix – Performance
Evaluation
The overall Accuracy rate is the
number of correct classifications
Predicted Class
divided by the total number of
classifications:
B (2) M (4)
TP+TN /
TP+TN+FP+FN
Error Rate = 1- Accuracy
B (2) TP FN
Act.
Not a correct measure if
Class
Unbalanced Dataset
M (4) FP TN
Classes are unequally
represented
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20. Unbalanced dataset problem
Solution: Stratified Sampling Method
Partitioning of dataset based on class
Random Sampling Process
Create Training and Test set with equal size class
Testing set data independent from Training set.
Standard Verification technique
Best error estimate
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21. Stratified Sampling Method
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22. Performance Evaluation
PERFORMANCE EVALUATION
Test Set
# # Act. Exp.
Dataset Instances Rules MAE Acc. Acc.
Rate Rate
476 13 2% 99% 97%
Training set
412 13 3% 96% 92%
Testing set
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23. Tree Visualization
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24. Unbalanced dataset Problem
Solution: Cost Matrix
Cost sensitive classification
Costs not known
Complete financial analysis needed; i.e cost of
Using ML tool
Gathering training data
Using the model
Determining the attributes for test
Cross Validation once all costs are known
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25. Future direction
The overall accuracy of the classifier needs to be
increased
Cluster based Stratified Sampling
Partitioning the original dataset using Kmeans Alg.
Multiple Classifier model
Bagging and Boosting techniques
ROC (Receiver Operating Characteristic)
Plotting the TP Rate (Y-axis) over FP Rate (X-Axis)
Advantage: Does not regard class distribution or
error costs.
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26. ROC Curve - Visualization
•Area under the curve AUC
•Larger the area, better is the model
For Benign class For Malignant class
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27. Questions / Comments
Thank You!
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