Analysis of existing options for the classification of patients with cardiovascular disease using neural networks
The work is devoted to the analysis of information technologies of chronobiological monitoring of cardiac systems, development of decision support system for physician-researcher based on classification methods using neural networks such as PNN (Probabilistic Neural Networks), multilayer perceptron and CasCor (Cascade Correlation).
The training sample was 80% of the total number of patients (278 training pairs), and the test sample was 20% (65 training pairs). The presented data sample is highly representative, considering the number of patients: dataset includes 343 patients with cardiovascular complications (class 1 - 142, class 2 - 207). The age range of patients in the sample is 50-60 years old.
The result is the following: the best classifier is the neural network of cascade correlation with 85-88% classification accuracy. The worst classifier was the probabilistic neural network, since the accuracy of this algorithm depends on the size of the data set.
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