Neural Network Classification versus Linear Programming Classification in breast cancer diagnosis

1. Neural Network Classification versus Linear Programming Classification in breast cancer diagnosis Denny Wibisono December 10, 2001

2. Outline • Problem Statement and Motivation • Neural network application in breast cancer diagnosis • Results

3. Problem Statement and Motivation • Problem: discriminate benign and malignant in an unknown sample from fine needle aspirates taken from patients’ breasts • Motivation: compare the performance of neural network classification with linear programming classification • Expectation: Neural networks classification can do better job classifying the data

4. Application • Data used: Wisconsin Breast Cancer Data (from class website) • Apply the KNN, SVM and BP algorithm to the data. • Data need to be modified • Used the programs given in class • Apply the Linear Programming algorithm to the data • Write a program similar with CS 525 project

5. Results • KNN: C_rate = 100.00 • BP: C_rate = 98.9071 • Linear Programming: C_rate = 98.8809 • SVM • As expected, the result for the neural network classification gives better classification rate than linear programming