Prognostic Comparison of Statistical, Neural and Fuzzy Methods of Analysis of Breast Cancer Image Cytometric Data.

摘要

This paper aims to predict a breast cancer patient's prognosis and to determine the most important prognostic factors by means of logistic regression (LR) as a conventional statistical method, multilayer backpropagation neural network (MLBPNN) as a neural network method, fuzzy K-nearest neighbor algorithm (FK-NN) as a fuzzy logic method, a fuzzy measurement based on the FK-NN and the leave-one-out error method. The data used for breast cancer prognostic prediction were collected from 100 women who were clinically diagnosed with breast disease in the form of carcinoma or benign conditions.