Analysis of existing options for the classification of patients with cardiovascular disease using neural networks

  • Bohdan Molodets
  • Тatyana Bulanaya


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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