Solving medical classification problems with RBF neural network and filter methods

J. Novakovic, A. Veljovic
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引用次数: 2

Abstract

This paper evaluates classification accuracy of radial basis function (RBF) neural network and filter methods for feature selection in medical datasets. To improve the diagnostic procedure in the daily routine and to avoid wrong diagnosis, machine learning methods can be used. Diagnosis of tumours, heart disease, hepatitis, liver and Parkinson's diseases are a few of the medical problems which we have used in artificial neural networks. The main objective of this paper is to show that it is possible to improve the performance of the system for inductive learning rules with RBF neural network for medical classification problems, using the filter methods for feature selections. The aim of this research is also to present and compare different algorithm approach for the construction system that learns from experience and makes decisions and predictions and reduce the expected number or percentage of errors.
用RBF神经网络和滤波方法解决医学分类问题
对径向基函数(RBF)神经网络和滤波方法在医学数据集特征选择中的分类精度进行了评价。为了改进日常诊断程序,避免误诊,可以使用机器学习方法。肿瘤、心脏病、肝炎、肝脏和帕金森氏症的诊断是我们在人工神经网络中使用的一些医学问题。本文的主要目的是表明,使用过滤器方法进行特征选择,可以提高系统对医学分类问题的归纳学习规则的性能。本研究的目的还在于提出并比较不同的算法方法,使建筑系统从经验中学习,做出决策和预测,减少预期的错误数量或百分比。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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