Design of improved probability neural network classifiers for medical decision making with the aid of genetic optimization algorithm

摘要

This paper concerns the design of classifiers for medical decision making, and proposes a novel probabilistic neural network classifier with the assistance of a genetic algorithm. Unlike the conventional probabilistic neural networks that use all patterns in data sets as hidden nodes, the proposed neural network adopts some centers through clustering algorithms and the output of data set as the hidden nodes. Comparing with the conventional probability neural network classifiers, the proposed approach significantly decreases the time consuming. Furthermore, a genetic algorithm is utilized to optimize feature selection from the data set. Experimental results are presented on several benchmarks illustrating the relationship between selected features and disease.