2011 Special Issue: Genetic algorithm pruning of probabilistic neural networks in medical disease estimation

Authors:
Dimitrios Mantzaris;George Anastassopoulos;Adam Adamopoulos
Affiliations:
Informatics Laboratory, Department of Nursing, Technological Educational Institute of Kavala, GR-68300, Didymoteicho, Greece;Medical Informatics Laboratory, Democritus University of Thrace, GR-68100, Alexandroupolis, Greece and Hellenic Open University, GR-26222, Patras, Greece;Medical Physics Laboratory, Democritus University of Thrace, GR-68100, Alexandroupolis, Greece and Hellenic Open University, GR-26222, Patras, Greece
Venue:
Neural Networks
Year:
2011

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Abstract

A hybrid model consisting of an Artificial Neural Network (ANN) and a Genetic Algorithm procedure for diagnostic risk factors selection in Medicine is proposed in this paper. A medical disease prediction may be viewed as a pattern classification problem based on a set of clinical and laboratory parameters. Probabilistic Neural Network models were assessed in terms of their classification accuracy concerning medical disease prediction. A Genetic Algorithm search was performed to examine potential redundancy in the diagnostic factors. This search led to a pruned ANN architecture, minimizing the number of diagnostic factors used during the training phase and therefore minimizing the number of nodes in the ANN input and hidden layer as well as the Mean Square Error of the trained ANN at the testing phase. As a conclusion, a number of diagnostic factors in a patient's data record can be omitted without loss of fidelity in the diagnosis procedure.