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Data Mining - Classification Of Breast Cancer Dataset using Decision Tree Induction - Sunil Nair Health Informatics Dalhousie University

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Sunil Nair
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Classification of Breast Cancer dataset using Decision Tree Induction Sunil Nair Abel Gebreyesus Masters of Health Informatics Dalhousie University HINF6210 Project Presentation – November 25, 2008
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Introduction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Objective ,[object Object],[object Object],[object Object]
Breast Cancer Dataset ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],Wisconsin Breast Cancer Database (1991) University of Wisconsin Hospitals, Dr. William H. Wolberg
Attributes ,[object Object],[object Object],Class Benign (2), Malignant (4) 11 1-10 Mitoses 10 1-10 Normal Nucleoli 9 1-10 Bland Chromatin 8 1-10 Bare Nuclei 7 1-10 Single Epithelial Cell Size 6 1-10 Marginal Adhesion 5 1-10 Uniformity of Cell Shape 4 1-10 Uniformity of Cell Size 3 1-10 Clump Thickness 2 id number Sample code number 1
Attributes / class - distribution ,[object Object]
Our Approach ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Data Pre-processing ,[object Object],[object Object],[object Object]
Data preprocessing ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Comparison chart – Handle Missing Value Confusion Matrix Total Correctly Classified Instances Test split = 223 Accuracy Rate: 95.78% How many predictions by chance? Expected Accuracy Rate = Kappa Statistic - is used to measure the agreement between predicted and actual categorization of data while correcting for prediction that occurs by chance. 89% 95% 7% 14 Missing Replaced 90% 96% 5% 11 Missing Removed 87% 94% 8% 14 Complete Exp. Acc. Rate Act. Acc. Rate MAE # RULES DATASET PERFORMANCE EVALUATION 233 70 163 Total 66 63 3 M 167 7 160 B Total M B Class
Data Pre-processing ,[object Object],Missing Value Removed - Mean-Mode
Agenda ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Classification Methods Comparison 94% 97% 3% 233 Support Vector M 92% 97% 4% 233 DT-J48 79% 91% 10% 233 Neural Network 90% 96% 4% 233 Naïve Bayes Exp. Acc. Rate Act. Acc. Rate MAE # Total Inst. CLASSIFIER PERFORMANCE EVALUATION Test Set
Classification using Decision Tree ,[object Object],[object Object],[object Object],[object Object],[object Object]
Attributes Selected – most IG weka.filters.supervised.attribute.AttributeSelection-Eweka.attributeSelection.InfoGainAttributeEval-Sweka.attributeSelection.Ranker 89% 95% 7% 14 Missing Replaced 90% 96% 5% 11 Missing Removed 92% 97% 4% 11 Attributes Selected Exp. Acc. Rate Act. Acc. Rate MAE # RULES DATASET PERFORMANCE EVALUATION 0.198 Mitosis 9 0.443 Marginal Adhesion 8 0.459 Clump Thickness 7 0.466 Normal Nucleoli 6 0.505 Single Epithelial Cell Size 5 0.543 Bland Chromatin 4 0.564 Bare Nucleoli 3 0.66 Uniformity of Cell Shape 2 0.675 Uniformity of Cell Size 1 Information Gain Attribute Rank
The DT – IG/Attribute selection Visualization
Decision Tree - Problems ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Confusion Matrix – Performance Evaluation ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],TN FP M (4) FN TP B (2) Act. Class M (4) B (2) Predicted Class
Unbalanced dataset problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Stratified Sampling Method
Performance Evaluation 92% 96% 3% 13 412 Testing set 97% 99% 2% 13 476 Training set Exp. Acc. Rate Act. Acc. Rate MAE # Rules # Instances Dataset PERFORMANCE EVALUATION Test Set
Tree Visualization
Unbalanced dataset Problem ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
Future direction ,[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object],[object Object]
ROC Curve - Visualization For Benign class For Malignant class ,[object Object],[object Object]
Questions / Comments Thank You !

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Data Mining - Classification Of Breast Cancer Dataset using Decision Tree Induction - Sunil Nair Health Informatics Dalhousie University

  • 1. Classification of Breast Cancer dataset using Decision Tree Induction Sunil Nair Abel Gebreyesus Masters of Health Informatics Dalhousie University HINF6210 Project Presentation – November 25, 2008
  • 2.
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  • 11. Comparison chart – Handle Missing Value Confusion Matrix Total Correctly Classified Instances Test split = 223 Accuracy Rate: 95.78% How many predictions by chance? Expected Accuracy Rate = Kappa Statistic - is used to measure the agreement between predicted and actual categorization of data while correcting for prediction that occurs by chance. 89% 95% 7% 14 Missing Replaced 90% 96% 5% 11 Missing Removed 87% 94% 8% 14 Complete Exp. Acc. Rate Act. Acc. Rate MAE # RULES DATASET PERFORMANCE EVALUATION 233 70 163 Total 66 63 3 M 167 7 160 B Total M B Class
  • 12.
  • 13.
  • 14. Classification Methods Comparison 94% 97% 3% 233 Support Vector M 92% 97% 4% 233 DT-J48 79% 91% 10% 233 Neural Network 90% 96% 4% 233 Naïve Bayes Exp. Acc. Rate Act. Acc. Rate MAE # Total Inst. CLASSIFIER PERFORMANCE EVALUATION Test Set
  • 15.
  • 16. Attributes Selected – most IG weka.filters.supervised.attribute.AttributeSelection-Eweka.attributeSelection.InfoGainAttributeEval-Sweka.attributeSelection.Ranker 89% 95% 7% 14 Missing Replaced 90% 96% 5% 11 Missing Removed 92% 97% 4% 11 Attributes Selected Exp. Acc. Rate Act. Acc. Rate MAE # RULES DATASET PERFORMANCE EVALUATION 0.198 Mitosis 9 0.443 Marginal Adhesion 8 0.459 Clump Thickness 7 0.466 Normal Nucleoli 6 0.505 Single Epithelial Cell Size 5 0.543 Bland Chromatin 4 0.564 Bare Nucleoli 3 0.66 Uniformity of Cell Shape 2 0.675 Uniformity of Cell Size 1 Information Gain Attribute Rank
  • 17. The DT – IG/Attribute selection Visualization
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  • 22. Performance Evaluation 92% 96% 3% 13 412 Testing set 97% 99% 2% 13 476 Training set Exp. Acc. Rate Act. Acc. Rate MAE # Rules # Instances Dataset PERFORMANCE EVALUATION Test Set
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  • 27. Questions / Comments Thank You !