Abstract
The problem of developing universal classifiers of biomedical data, in particular those that characterize the presence of a large number of parameters, inaccuracies and uncertainty, is urgent. Many studies are aimed at developing methods for analyzing these data, among them there are methods based on a neural network (NN) in the form of a multilayer perceptron (MP) using GA. The question of the application of evolutionary algorithms (EA) for setting up and learning the neural network is considered. Theories of neural networks, genetic algorithms (GA) and decision trees intersect and penetrate each other, new developed neural networks and their applications constantly appear. An example of a problem that is solved using EA algorithms is considered. Its goal is to develop and research a classifier for the diagnosis of breast cancer, obtained by combining the capabilities of the multilayer perceptron using the genetic algorithm (GA) and the CART decision tree. The possibility of improving the classifiers of biomedical data in the form of NN based on GA by applying the process of appropriate preparation of biomedical data using the CART decision tree has been established. The obtained results of the study indicate that these classifiers show the highest efficiency on the set of learning and with the minimum reduction of Decision Trees; increasing the number of contractions usually degrades the simulation result. On two datasets on the test set, the simulation accuracy was »83–87 %. The experiments carried out have confirmed the effectiveness of the proposed method for the synthesis of neural networks and make it possible to recommend it for practical use in processing data sets for further diagnostics, prediction, or pattern recognition
Highlights
Researchers have recognized neural networks (NNs) as a tool for solving many problems related to biomedicine and the health care system
It should be considered expedient to study a method for developing a neural network based on genetic algorithm (GA) by combining the optimization of the NN weights and the application of preliminary data analysis based on the classification and regression tree (CART) classification trees
This reduces the number of inputs to the neural network and improves the accuracy of the simulation
Summary
Researchers have recognized neural networks (NNs) as a tool for solving many problems related to biomedicine and the health care system. Neural networks “are able to study the relationship between input-output mapping on a given sample of data without any prior knowledge or assumptions about the statistical distribution of the data This ability to learn from data without any prior knowledge makes the NN suitable for solving practical problems of classification and regression. It is urgent to solve the issues related to the problem of developing classifiers of biomedical data in the form of NNs based on GA, which characterize the presence of a large number of parameters, inaccuracies and uncertainty. The results of such studies will provide effective systems for their application in the health care system
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