Presentation on theme: "FUZZ-IEEE 2009 Methods of Interpretation of a Non-stationary Fuzzy System for the Treatment of Breast Cancer Xiao-Ying Wang 1, Jonathan M. Garibaldi 1,"— Presentation transcript:
FUZZ-IEEE 2009 Methods of Interpretation of a Non-stationary Fuzzy System for the Treatment of Breast Cancer Xiao-Ying Wang 1, Jonathan M. Garibaldi 1, Shang-Ming Zhou 2, Robert I. John 2 1 The University of Nottingham, Nottingham, UK 2 De Montfort University, Leicester, UK Speaker: Dr. Xiao-Ying Wang (Sally) Supervisor: Dr. Jon Garibaldi
Breast Cancer treatment decision making Multidisciplinary team (oncologist, radiologist, surgeon, pathologist) Computational intelligence techniques in breast cancer diagnosis and decision making Computational intelligence techniques Uncertain and imprecise terms Traditional fuzzy methods (Type-1, Type-2) Non-stationary fuzzy sets Introduction
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 Decisions Hormone therapy Radiotherapy Chemotherapy Further operation Follow up Data Description (3)
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
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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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