Breast Cancer Diagnosis using a Hybrid Genetic Algorithm for Feature Selection based on Mutual Information (Abeer Alzubaidi, Georgina Cosma, David Brown and Graham Pockley) Interactive Technologies and Games (ITAG) Conference 2016 Health, Disability and EducationDates: Wednesday 26 October 2016 - Thursday 27 October 2016 Location: The Council House, NG1 2DT

1. Breast Cancer Diagnosis using a Hybrid Genetic
Algorithm for Feature Selection based on
Mutual Information
Abeer Alzubaidi
(PhD researcher)
School of Science and Technology
Nottingham Trent University

2. • What is Breast Cancer?
• Breast Cancer Diagnosis
• Statistical Methods
• Predictive Modelling
• Evolutionary Computation
• The Hybrid Genetic Approach
• Breast Cancer Dataset
• The results
• Current Work & Conclusion
2
Content

3. What is the Breast Cancer?
• Breast Cancer begins in the
breast tissue and may start
in the duct or lobe of the
breast when the “controls”
in the breast cells are not
working properly, they divide
continually and a lump or
tumor is formed.
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4. • Breast cancer is the most common cancer in women in both
developed and developing countries.
• The number of breast cancer cases worldwide was estimated at
14.1 million new cases and 8.2 million deaths in 2012.
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Breast Cancer Statistics
Article Source: Model Comparison for Breast Cancer Prognosis Based on Clinical Data

5. Breast Cancer Diagnosis
• Successful early detection
– Better treatments to patients.
– Better clinical decision making.
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6. • Statistical methods are the most popular approaches used in
clinical practice for cancer diagnosis and prognosis.
• Statistical Methods Challenges
o Data Diversity
o High dimensional data.
o The uncertainty and imprecision
o Relevancy & Redundancy
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Statistical Methods

7. • Predictive modelling in medicine involves deriving a
mathematical model for the prediction of an outcome for
future patients.
• Our goal is to classify two types of tumors for breast cancer
diagnosis, i.e. if the cancer is Malignant or if it is Benign.
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Predictive Modelling

8. Generating a good
Model
Accurate Stable General
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Prediction Model Challenges

9. • Evolutionary Algorithms are suitable for constructing good
predictive models.
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Evolutionary Computation

10. 10
The Hybrid Genetic Approach
• The proposed method is the
combination of a Genetic Algorithm
(GA) based on Mutual Information
(MI) for identifying cancer predictors.
• Genetic algorithm iterates through
the combinations of features. The best
set of features (i.e. predictors) is then
selected statistically and passed
through the ML classifier.
• Prediction is based the knowledge
which has been acquired by the model
during the learning process.

11. • This study used the Wisconsin Breast Cancer dataset.
• The dataset is provided by university of Wisconsin hospital,
Madison.
• The dataset contains records collected from 699 patients.
• According to the class distribution 458 (65.5%) cases were
derived from patients with a benign tumor and 241 (34.5%)
cases were derived from patients with a malignant tumor.
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Breast Cancer Datasets

12. The Attribute Information For
Breast Cancer Datasets
Feature name Range
1 Clump thickness 1-10
2 Uniformity of cell size 1-10
3 Uniformity of cell shape 1-10
4 Marginal adhesion 1-10
5 Single epithelial cell size 1-10
6 Bare nuclei 1-10
7 Bland chromatin 1-10
8 Normal nucleoli 1-10
9 Mitoses 1-10
10 Diagnosis 0 for benign, 1 for malignant.
Article Source : Multisurface method of pattern separation for medical diagnosis applied to breast cytology
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13. Leave-One-Out Cross Validation
(LOOCV)
• Breast cancer dataset contained 699 patient
cases.
• Evaluations using cross validation: A total of
699 iterations. In each iteration 699-1 patient
cases were used for training and the
remaining one case was used for testing. This
is the most acceptable approach in the
clinical literature.
• Eventually, all patient cases are passed
through the testing process.
• Performance of the algorithm is based on its
predictive accuracy to detect the test cases
(i.e. all previously unseen patient records)
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14. 14
Experimental Results Using
The SVM Classifier
Evaluation
Measures
SVM - Kernel Functions
RBF Linear Quadratic MLP
5 Features
Correct Rate 0.9820 0.9822 0.9844 0.9795
AUC 0.9605 0.9659 0.9669 0.9508
ORP FPR 0.0332 0.0332 0.0290 0.0373
ORP TPR 0.9541 0.9651 0.9629 0.9389
6 Features
Correct Rate 0.9778 0.9823 0.9844 0.9683
AUC 0.9607 0.9681 0.9669 0.9382
ORP FPR 0.0415 0.0332 0.0290 0.0581
ORP TPR 0.9629 0.9694 0.9629 0.9345
7 Features
Correct Rate 0.9822 0.9845 0.9800 0.9909
AUC 0.9648 0.9702 0.9617 0.9688
ORP FPR 0.0332 0.0290 0.0373 0.0166
ORP TPR 0.9629 0.9694 0.9607 0.9541

15. 15
Experimental Results Using
The k-NN Classifier
Evaluation
Measures
k-NN Distance Measures
Correlation Minkowski Euclidean Seuclidean
7 Features
Correct Rate 0.9605 0.9887 0.9887 0.9865
AUC 0.9156 0.9678 0.9678 0.9679
ORP FPR 0.9017 0.0207 0.0207 0.0249
ORP TPR 0.9017 0.9563 0.9563 0.9607
8 Features
Correct Rate 0.9624 0.9843 0.9843 0.9844
AUC 0.9133 0.9658 0.9658 0.9680
ORP FPR 0.8930 0.0290 0.0290 0.0290
ORP TPR 0.8930 0.9607 0.9607 0.9651
9 Features
Correct Rate 0.9559 0.9910 0.9910 0.9888
AUC 0.9104 0.9731 0.9731 0.9733
ORP FPR 0.8996 0.0166 0.0166 0.0207
ORP TPR 0.8996 0.9629 0.9629 0.9672

16. • Developed a hybrid approach to detecting breast cancer
based on Genetic Algorithm and Mutual Information.
• Experiments were performed to evaluate the performance of
proposed approach with two different machine learning
classifiers, K-NN, and SVM, each tuned using different
distance measures and kernel functions, respectively.
• The results revealed that the proposed hybrid approach is
highly accurate for predicting breast cancer.
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Current Work

17. 17
Conclusion

18. 18
• Director of Studies:
– Dr Georgina Cosma georgina.cosma@ntu.ac.uk
• Supervisory Team:
– Professor Graham Pockley graham.pockley@ntu.ac.uk
– Professor David Brown david.brown@ntu.ac.uk
TEAM

19. 19
Acknowledgements
• Support: Funding received from Ministry of High
Education and Scientific Research in Iraq.

20. Thank you
Any questions?