Abstract
Artificial neural networks are finding many uses in the medical diagnosis application. The article examines cases of renopathy in type 2 diabetes. Data are symptoms of disease. The multilayer perceptron networks (MLP) is used as a classifier to distinguish between a sick and a healthy person. The results of applying artificial neural networks for diagnose renopathy based on selected symptoms show the network's ability to recognize to recognize diseases corresponding to human symptoms. Various parameters, structures and learning algorithms of neural networks were tested in the modeling process.
Highlights
The task of diagnosis is to identify a disease that a patient has with certain symptoms
Using information about a patient's condition in the mathematical model the probable diagnosis can be determined. These mathematical models are based on statistical distributions, regression models and artificial intelligence [1,2,3]
We will use multilayer perceptron networks that are capable of correctly classifying nonlinear separable input data sets required for diagnostics
Summary
The task of diagnosis is to identify a disease that a patient has with certain symptoms. «Системні технології» 2 (133) 2021 «System technologies» In supervised learning, the network is trained by providing it with input and output patterns During this phase, the neural network is able to adjust the connection weights to match its output with the actual output in an iterative process until a desirable result is reached. The patient’s data are processed by the neural network, which determines the probable diagnosis This result is validated by the attending physician. This work attempts to test various parameters and network structure for their suitability for recognizing this disease To solve these problems, we will use multilayer perceptron networks that are capable of correctly classifying nonlinear separable input data sets required for diagnostics. A multilayer perceptron with two neurons in the output layer is used: one indicates the presence of a disease, and the other indicates the absence
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