Liver Disease Diagnosis Based on Neural Networks EBENEZER

Abstract

In this paper, two models of artificial neural network have been developed to solve the problems facing physicians in diagnosis of liver diseases. Experience has shown that many patients suffering from liver disorder die daily as a result of misdiagnosis of the diseases. Therefore, two models: back propagation neural network and radial basis function neural network are designed to diagnose these diseases and also prevent misdiagnosis of the liver disorder patients. These systems are developed using the BUPA liver disorder dataset obtained from UCI machine learning repository. The dataset is made up of 6 attributes which are major factors that cause liver disorder in patients. The results obtained from testing of the networks were compared with each other. Also, with the previous research on liver disorder using the same dataset to ascertain the best network needed for diagnosis of the disease. Key-Words: Liver disorder, BUPA dataset, Radial basis function, Back propagation neural network, Data mining, machine learning

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Cite this paper

@inproceedings{Olaniyi2015LiverDD, title={Liver Disease Diagnosis Based on Neural Networks EBENEZER}, author={O. E. Olaniyi and KHASHMAN ADNAN}, year={2015} }