Power Point Template

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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, UK 2 De
Montfort University, Leicester, UK
Speaker Dr. Xiao-Ying Wang
(Sally) Supervisor Dr. Jon Garibaldi
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Outline
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• 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

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An example of a non-stationary fuzzy set with
multiple instantiations
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An example of a non-stationary fuzzy set with
multiple instantiations
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An outline of a non-stationary fuzzy inference
system
?
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• Breast cancer post operative (adjuvant)
• treatment decision data
• From City Hospital Nottingham Breast Institute
  (multidisciplinary team)
• Attributes Treatment decisions
• (1310 real patients cases)

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• 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

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• Treatment Decisions
• Hormone therapy
• Radiotherapy
• Chemotherapy
• Further operation
• Follow up

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Clinical guideline for adjuvant therapy following
surgery
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Fuzzy rules derived directly from the clinical
guidelines
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(No Transcript)
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• No 0, 55
• Maybe (55,56
• Yes (56, 100)

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

Agreement (9822124)/1310 84.6
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• 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-avg
Sum-avg
Majority
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NS FS Output Processing (2)
Ns-avg
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Sum-avg
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Majority
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The number of agreements obtained over a range of
variation for the three output processing methods
1150 1140 1130 1120 1110 1100 1000
Majority
No. of Agreement
Ns-avg
Sum-avg
0 0.01 0.02 0.03 0.04 0.05 0.06
0.07 0.08 0.09 0.1
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The best confusion matrices obtained for the
three different methods of Output Interpretation
Ns-avg
Sum-avg
Majority
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Advantage on output of NS FS

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

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• 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

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• Represent variation within FIS
• Variation comparison between FIS and real
  clinical experts
• Potential other output processing methods in NS FS

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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. 7585, 1997.
• C. A. Pena-Reyes and M. Sipper, A fuzzy-genetic
  approach to breast cancer diagnosis, Artificial
  Intelligence in Medicine, vol. 17, pp. 131135,
  1999.
• H. A. Abbass, An evolutionary artificial neural
  networks approach for breast cancer diagnosis,
  Artificial Intelligence in Medicine, vol. 23, no.
  3, pp. 265181, 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/SWQN05), Maryland, USA, 2005,
  pp. 8287.
• 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. 14691475.
• J. M. Garibaldi, M. Jaroszewski, and S.
  Musikasuwan, Non-stationary fuzzy sets, IEEE
  Transations on Fuzzy Systems, vol. 16 (4), pp.
  10721086, 2008.

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Thank You!
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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

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How to process the output of NS FS
Average
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Whats NS FS ?

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