Survey
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
* Your assessment is very important for improving the workof artificial intelligence, which forms the content of this project
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 FUZZ-IEEE 2009 Outline Conclusions Introduction Nonstationary FS Experiments Data Description Type-1 FS FUZZ-IEEE 2009 NS FS Output Processing Introduction • 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 Nonstationary FS (2) An example of a non-stationary fuzzy set with multiple instantiations mf (x,2,5,8) mf ( x, p1 (t ), p2 (t ), p3 (t )) mf (x,2,5,8) An example of a non-stationary fuzzy set with multiple instantiations Nonstationary FS (3) Perturb functions f1 (t ) f 2 (t ) f 3 (t ) f (t ) Normal distributi on function 0.02 k1 k 2 k3 k 1 n 20 Nonstationary FS (1) An outline of a non-stationary fuzzy inference system ? Data Description (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 (2) • 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 (3) • Treatment Decisions Hormone therapy Radiotherapy Chemotherapy Further operation Follow up Clinical guideline for adjuvant therapy following surgery Data Description (4) Type-1 FS (3) Fuzzy rules derived directly from the clinical guidelines Type-1 FS (1) Type-1 FS (2) No [0, 55] Maybe (55,56] Yes (56, 100) Type-1 FS (4) • Confusion matrix obtained by the original type-1 fuzzy system Agreement: (982+2+124)/1310 = 84.6% NS FS Output Processing (1) • Type-1 fuzzy system (FS) Non-stationary FS • Perturbation function – normal distribution standard deviation iteration = 30 0.01, 0.02, ... 0.1 • Output processing methods: – Existing non-stationary FS output approach Sum-avg – method – method Majority Ns-avg Ns-avg NS FS Output Processing (2) NS FS Output Processing (3) Sum-avg NS FS Output Processing (4) Majority Experiments (1) The number of agreements obtained over a range of variation for the three output processing methods 1150 No. of Agreement 1140 Majority 1130 Ns-avg 1120 1110 1100 Sum-avg 1000 0 0.01 0.02 0.03 0.04 0.05 0.06 0.07 0.08 0.09 0.1 Experiments (2) The best confusion matrices obtained for the three different methods of Output Interpretation Ns-avg Sum-avg Majority Advantage on output of NS FS • Improvement of accuracy • Best no. of agreement achieved on sd = 0.08 Conclusions • • • • 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 Future work • Represent variation within FIS • Variation comparison between FIS and real clinical experts • Potential other output processing methods in NS FS 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