FUZZ-IEEE 2009

Methods of Interpretation of a Non-stationary Fuzzy System for the Treatment of Breast Cancer

Xiao-Ying Wang1, Jonathan M. Garibaldi1, Shang-Ming Zhou2, Robert I. John2

1 The University of Nottingham, Nottingham, UK2 De Montfort University, Leicester, UK

Speaker: Dr. Xiao-Ying Wang (Sally)Supervisor: Dr. Jon Garibaldi

FUZZ-IEEE 2009

Outline

IntroductionIntroduction

Data Description

Non-stationary FS

Type-1 FS

NS FS Output NS FS Output ProcessingProcessing

Experiments

Conclusions

• Breast Cancer treatment decision making• Multidisciplinary team

(oncologist, radiologist, surgeon, pathologist)

• Computational intelligence techniques in breast cancer diagnosis and decision making

• Uncertain and imprecise terms • Traditional fuzzy methods (Type-1, Type-2)• Non-stationary fuzzy sets

IntroductionIntroduction

An example of a non-stationary fuzzy set with multiple instantiations

)8,5,2,(xmf

)8,5,2,(xmf

Non-stationary FS

(2)

))(),(),(,( 321 tptptpxmf

An example of a non-stationary fuzzy set with multiple instantiations

20

1

0.02function on distributi Normal

)()()()(

functions Perturb

321

321

n

kkkk

tftftftf

Non-stationary FS

(3)

An outline of a non-stationary fuzzy inference system

Non-stationary FS

(1)

?

• Breast cancer post operative (adjuvant) treatment decision data • From City Hospital Nottingham Breast Institute

(multidisciplinary team)

Attributes + Treatment decisions

(1310 real patients cases)

Data Description

(1)

• Attributes: Patients’ age Lymph node stage, the number of positive lymph

node found from samples Nottingham prognostic index (NPI) value -an indication of how successful treatment might be

-NPI = (0.2 x tumour diameter in cms) + lymph node stage + tumour grade Estrogen receptor (ER) test result Vascular invasion test result

Data Description

(2)

• Treatment DecisionsHormone therapyRadiotherapyChemotherapyFurther operationFollow up

Data Description

(3)

Clinical guideline for adjuvant therapy following surgery

Data Description

(4)

Fuzzy rules derived directly from the clinical guidelines

Type-1 FS(3)

Type-1 FS(1)

No [0, 55]Maybe (55,56] Yes (56, 100)

Type-1 FS(2)

• Confusion matrix obtained by the original type-1 fuzzy system

Type-1 FS(4)

Agreement:

(982+2+124)/1310 = 84.6%

• Type-1 fuzzy system (FS) Non-stationary FS• Perturbation function – normal distribution

standard deviation iteration = 30

• Output processing methods:– Existing non-stationary FS output approach– method– method

NS FS Output NS FS Output ProcessingProcessing

(1)(1)

0.1 ... 0.02, ,01.0

MajorityMajority

Sum-avgSum-avg

Ns-avgNs-avg

Ns-avgNs-avg

NS FS Output NS FS Output ProcessingProcessing

(2)(2)

NS FS Output NS FS Output ProcessingProcessing

(3)(3)

Sum-avgSum-avg

NS FS Output NS FS Output ProcessingProcessing

(4)(4)MajorityMajority

The number of agreements obtained over a range of variation for the three output processing methods

Experiments(1)

MajorityMajority

Sum-avgSum-avg

Ns-avgNs-avg

0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1

No.

of

Agr

eem

ent

1150

1140

1130

1120

1110

1100

1000

The best confusion matrices obtained for the three different methods of Output Interpretation

Experiments(2)

Ns-avgNs-avg Sum-avgSum-avg MajorityMajority

Advantage on output of NS FS

• Improvement of accuracy• Best no. of agreement achieved on sd = 0.08

• Breast cancer follow up (adjuvant) treatment • Type-1, Type-2, non-stationary FS• Non-stationary FS applies to decision making• Proposed two new ways to interpret NS FS

Output processing.• Majority method improves the accuracy of a

NS FS

Conclusions

• Represent variation within FIS• Variation comparison between FIS and real

clinical experts• Potential other output processing methods in

NS FS

Future work

References• B. Kovalerchuk, E. Triantaphyllou, J. F. Ruiz, and J. Clayton, “Fuzzy logic in computer-aided

breast cancer diagnosis: Analysis of lobulation,” Artificial Intelligence in Medicine, vol. 11, no. 1, pp. 75–85, 1997.

• C. A. Pena-Reyes and M. Sipper, “A fuzzy-genetic approach to breast cancer diagnosis,” Artificial Intelligence in Medicine, vol. 17, pp. 131–135, 1999.

• H. A. Abbass, “An evolutionary artificial neural networks approach for breast cancer diagnosis,” Artificial Intelligence in Medicine, vol. 23, no. 3, pp. 265–181, 2002.

• X. Xiong, Y. Kim, Y. Baek, D. W. Rhee, and S.-H. Kim, “Analysis of breast cancer using data mining and statistical techniques,” in Proceedings of 6th Intelligence Conference on Software Engineering (SNPD/SWQN’05), Maryland, USA, 2005, pp. 82–87.

• S.-M. Zhou, R. I. John, X.-Y. Wang, J. M. Garibaldi, and I. O. Ellis, “Compact fuzzy rules induction and feature extraction using SVM with particle swarms for breast cancer treatments,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC 2008), Hong Kong, China, 2008, pp. 1469–1475.

• J. M. Garibaldi, M. Jaroszewski, and S. Musikasuwan, “Non-stationary fuzzy sets,” IEEE Transations on Fuzzy Systems, vol. 16 (4), pp. 1072–1086, 2008.

FUZZ-IEEE 2009

Literature

• Fuzzy sets to represent the opinions for radiologists in analysing two important features from the American College of Radiology Breast Imaging Lexicon [Kovalerchuk et al 1997]

• Fuzzy-genetic method to Wisconsin BC diagnosis data. Genetic algorithm was used to generate a fuzzy inference system [Pena-Reyes and Sipper 1999]

• Evolutionary arificial neural network for BC diagnosis [Abbass 2002]

• Data mining for decision trees and association rules to discover unsuspected relationship within BC data [Xiong 2005]

• Particle swarming optimisation within a support vector machine for recommending treatments in BC [Zhou et al 2008]

How to process the output of NS FS

Average

What’s NS FS ?

A fuzzy system where the variability is introduced through the random alterations to the parameters of the membership functions

over time