1. Breast Cancer Diagnosis A discussion of methods Meena Vairavan

2. Presentation Outline n Problem Statement n Description of Data n Methods of Diagnosis n Future Plans

3. Problem Statement My goal is to compare two computational methods to determine which is a more effective means for performing breast cancer diagnosis. The first method uses linear programming and the second method uses neural networks. Both methods analyze data generated by fine needle aspiration tests.

4. Description of Data n Source of Data: Wisconsin Diagnosis Breast Cancer Database (WBCD) –Dr. William Wolberg - Department of Surgery –Professor W. Nick Street - Department of Manag. Sciences –Professor O.L. Mangasarian - CS Department n Each case is represented by a 30-dim. feature vector computed from a digitized fine needle aspirate of a breast mass. n The features describe characteristics of the cell nuclei present in the image. –Radius, texture, smoothness, concavity, and symmetry

5. Methods of Diagnosis n Method 1: Diagnosis through linear programming via generation of a separation plane. n Method 2: Diagnosis through the use of a multi-layer perceptron model using back propagation techniques.

6. Diagnosis Through LP n A linear function was constructed to generate a separation plane to classify malignant and benign tumors. –f(x) =  ’x -  –f(x) > 0 for malignant cases –f(x) < 0 for benign cases –minimize misclassified points by choosing  and  to minimize distance from f(x)

7. Future Plans n Find the optimal MLP configuration for this diagnosis. –Plan to modify Professor Hu’s back propagation method for these purposes. n Choose appropriate characteristics to compare the LP method and MLP method n Provide an analysis of my results.