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Evolutionary Neural Logic Networks for Breast Cancer Diagnosis A.Tsakonas 1, G. Dounias 2, E.Panourgias 3, G.Panagi 4 1 Aristotle University of Thessaloniki,

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Presentation on theme: "Evolutionary Neural Logic Networks for Breast Cancer Diagnosis A.Tsakonas 1, G. Dounias 2, E.Panourgias 3, G.Panagi 4 1 Aristotle University of Thessaloniki,"— Presentation transcript:

1 Evolutionary Neural Logic Networks for Breast Cancer Diagnosis A.Tsakonas 1, G. Dounias 2, E.Panourgias 3, G.Panagi 4 1 Aristotle University of Thessaloniki, 2 University of the Aegean, 3 Euroclinic Hospital, 4 General Hospital of Chios “Skylitseion” Greece

2 WCAO, Crete,October,2004 Contents Cytological Breast Cancer Diagnosis Neural-Symbolic Systems & Hybrid C.I. Neural Logic Networks (NLNs) Evolutionary CI: Genetic Programming (GP) NLNs built from data: Evolutionary NLNs: ENLN Presentation of Results / Discussion Future Work

3 WCAO, Crete,October,2004 From Computational Intelligence to Evolutionary Neural Logic Networks Computational Intelligence (CI) Fuzzy Logic, Neural Networks, Evolutionary Comp., Machine Learning, etc. Hybrid Systems (Combination of NN, ML, EC, FL, etc.) Inductive and Logic Based Systems (ML, FS) Evolutionary and Neural Computing Techniques (EC, NN) Combined C.I. Hybrid Systems (e.g. ENLN) C.I. and Data Transformation Techniques (e.g. Wavelets)

4 WCAO, Crete,October,2004 Neural Logic Networks Finite directed graph Consisted by a set of input nodes and an output node The possible value for a node can be one of three ordered pair activation values (1,0) for true, (0,1) for false and (0,0) for don't know

5 # NNs (since the 60’s): high accuracy but no interpretability # NLNs(in the 90’s) : NN + Logic Theory (3-valued logic) # 3-valued logic: yes, no, don’t-know # (Grammar-guided) GP: Searching for solutions by evolution # ENLNs: NLN + GP (GP-driven NLNs) # The GP finds the best possible NLN in the form of rules #  (comparable) accuracy + (better) comprehensibility

6 WCAO, Crete,October,2004 Expressing NLNs into PROLOG rules We may create rules into the programming language PROLOG directly by every neural logic network.

7 WCAO, Crete,October,2004 Constructing NLNs from data : the Evolutionary NLNs

8 WCAO, Crete,October,2004 Breast Cancer Diagnosis Data collected from Medical University of Winsconsin (696 records, 9 continuous features) O. L. Mangasarian and W. H. Wolberg, Cancer diagnosis via linear programming, SIAM News, Volume 23, Number 5, September 1990, pp O. L. Mangasarian, R. Setiono, W.H. Wolberg, Pattern recognition via linear programming: Theory and application to medical diagnosis", in: Large-scale numerical optimization, Thomas F. Coleman and Yuying Li, editors, SIAM Publications, Philadelphia 1990, pp

9 WCAO, Crete,October,2004 Data and system setup

10 WCAO, Crete,October,2004 Data Features

11 WCAO, Crete,October,2004 Results Highest classification accuracy obtained: 94.25% in unknown data ENLN selected not to use the Marginal Adhesion feature Other competitive techniques achieve % (e.g. nearest neighbour algorithm) (CNLN (P1 (P1 (In T3) (S1 (In T7) (Rule 0 0) E)) (P1 (P1 (P1 (P1 (P1 (In T1) (S1 (In T5) (Link (Rule 6 6)) E)) (S1 (S1 (In T2) (Link (Link (Rule 0 0))) E) (Link (Rule 0 0)) E)) (S1 (In T6) (Rule 0 0) E)) (P1 (S1 (In T1) (Rule 0 0) (S2 E (Rule 0 0) (S2 E (Rule 0 0) E))) (S1 (P1 (S1 (In T2) (Rule 0 0) (P2 (S2 E (Rule 0 0) E) (Rule 0 0) E)) (P1 (In T8) (S1 (In T6) (Rule 6 6) E))) (Rule 0 0) (P2 E (Rule 0 0) (S2 (S2 E (Rule 2 4) E) (Rule 0 0) E))))) (P1 (P1 (In T3) (S1 (In T7) (Rule 0 0) E)) (P1 (P1 (In T1) (In T7)) (S1 (In T2) (Link (Rule 0 0)) E))))) (Rule 6 6))

12 WCAO, Crete,October,2004 Summary We applied a “hybrid CI” approach in a breast cancer database Data: cytological information from past (diagnosed) cases We proposed an evolutionary computation-based neural logic technique for breast cancer diagnosis that maintains: High classification rate (94.25%) Some solution interpretability Potential knowledge extraction Discussion: some attributes are extensively used to construct the classifier, whereas some other are not selected at all. Future Directions: More experiments are needed (simpler solutions) Inductive decision trees might perform slightly worse in accuracy but the results are much more comprehensible


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