Title: Solving medical classification problems with RBF neural network and filter methods

Authors: Jasmina Ð Novakovic; Alempije Veljovic

Addresses: Belgrade Business School, Higher Education Institution for Applied Science, Belgrade, Serbia ' Faculty of Technical Science Cacak, University of Kragujevac, Cacak, Serbia

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.

Keywords: medical classification problems; classification accuracy; feature selection; filter methods; machine learning; RBF neural network.

DOI: 10.1504/IJRIS.2017.10009600

International Journal of Reasoning-based Intelligent Systems, 2017 Vol.9 No.2, pp.80 - 89

Received: 26 Feb 2016
Accepted: 01 Jul 2016
Published online: 14 Dec 2017 *

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