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Neural Network Classification versus Linear Programming Classification in breast cancer diagnosis. Denny Wibisono December 10, 2001. Outline. Problem Statement and Motivation Neural network application in breast cancer diagnosis Results. Problem Statement and Motivation.

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## Neural Network Classification versus Linear Programming Classification in breast cancer diagnosis

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**Neural Network Classification versus Linear Programming**Classification in breast cancer diagnosis Denny Wibisono December 10, 2001**Outline**• Problem Statement and Motivation • Neural network application in breast cancer diagnosis • Results**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**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**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

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