Neural Networks for the Tasks of Analytical Support of Public Health and Environment Monitoring Systems

Abstract

Introduction: Ensuring a further improvement of efficiency of the public health monitoring system requires integration of the modern health risk analysis methodology with a complex of adapted unified traditional and innovative analytical methods and data exchange with the environmental monitoring system. Objectives: The study aimed to test and assess the accuracy of predicting the incidence of malignant neoplasms using an artificial neural network. Materials and methods: The analyzed time series are presented by information from statistical reporting forms on malignant neoplasms in the city of Taganrog, Rostov Region. We applied a regression model and a forecasting modeling technique based on a feedforward artificial neural network of a multilayer perceptron type. An artificial neural network with 117 neurons in a hidden layer was created in the environment of the Matlab R2021a application package with a set of tools for the synthesis and analysis of neural networks Neural Network Toolbox using the Levenberg-Marquardt algorithm for its learning. Results: Approbation of two forecasting models was carried out on learning samples of different duration including 15 and 34 years. In a comparative assessment of the accuracy of forecasts for 2018 and 2019, absolute and relative errors were estimated. The accuracy of the neural network forecasting model was higher than that of the regression model both for the total of malignant neoplasms and for most cancer sites. The absolute errors of forecasts for 2018 when using regression and neural network models were 17.05 and 1.49 per 100,000 population, for 2019 – 39.07 and 4.42, respectively. The prediction accuracy dropped with a decrease in the time series duration and an increase in the distance from the boundaries of the learning sample. Conclusions: The feedforward artificial neural network of the multilayer perceptron type provides more accurate predictions using minimal input information compared to the regression model, which is its undoubted advantage.