Abstract
The aim of the paper was to develop a universal classifier in the form of a radial basis function network (RBF network) based on the Gaussian function and the CART Solution Tree. The examples of diseases diagnostics classifier were considered. It is noted that during the classifier development, it is necessary to determine the number of RBF neurons and the values of parameters of these neurons (centre, dispersion). For this purpose, a method that allows splitting the space of features into relatively homogeneous domains in the form of hyperparallelepipeds, each of which is associated with one of the RBF neurons, is proposed. The number of RBF neurons and parameters of these neurons are determined automatically directly based on the CART Solution Tree. As a result of the research, it was found that the proposed classifiers show the highest efficiency on the learning set with a minimal Solution Tree reduction (accuracy from 80 % to 95 %). It was shown that for two and more classes the accuracy of these classifiers on the test set makes 79 % and more, however, provided that the appropriate data sample for the learning set is selected. The possibility of using the RBF network based on the Gaussian function and the CART Solution Tree in the healthcare system for the diseases diagnostics and medical systems (or devices) assistance during decision-making support was proved. The obtained results could be further applied to improve the universal classifier development method based on the RBF network
Highlights
Nowadays, there are many algorithms that implement Solution Trees
This classifier has the form of RBF network and combines various methods of processing the information about the same object of research
The universal classifier development method is described and implemented on the example of the diseases diagnostics classifier that was obtained by combining the possibilities of a radial basis function network (RBF network) based on the Gaussian function and the Classification and Regression Trees (CART) Solution Tree
Summary
There are many algorithms that implement Solution Trees. In the late 1970s and early 1980s, J. The neural networks are able to study the relationship between the input-output mapping on a given sample of data without any prior knowledge or assumptions about statistical data distribution This ability of data learning without any prior knowledge makes the NN suitable for solving the practical problems of classification and regression. The neural networks are used in many real-world problems, including biomedicine, in order to surpass statistical classifiers and multiple regression methods during data analysis. Let us consider the problem of developing a universal classifier of diseases diagnostics This classifier has the form of RBF network and combines various methods of processing the information about the same object of research (classifier in the form of CART solution tree and classifier in the form of RBF network based on the Gaussian function)
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