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<abstract> <p>The paper considers the problem of handling short sets of medical data. Effectively solving this problem will provide the ability to solve numerous classification and regression tasks in case of limited data in health decision support systems. Many similar tasks arise in various fields of medicine. The authors improved the regression method of data analysis based on artificial neural networks by introducing additional elements into the formula for calculating the output signal of the existing RBF-based input-doubling method. This improvement provides averaging of the result, which is typical for ensemble methods, and allows compensating for the errors of different signs of the predicted values. These two advantages make it possible to significantly increase the accuracy of the methods of this class. It should be noted that the duration of the training algorithm of the advanced method remains the same as for existing method. Experimental modeling was performed using a real short medical data. The regression task in rheumatology was solved based on only 77 observations. The optimal parameters of the method, which provide the highest prediction accuracy based on MAE and RMSE, were selected experimentally. A comparison of its efficiency with other methods of this class has been performed. The highest accuracy of the proposed RBF-based additive input-doubling method among the considered ones is established. The method can be modified by using other nonlinear artificial intelligence tools to implement its training and application algorithms and such methods can be applied in various fields of medicine.</p> </abstract>

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Background: Today, using of systems on the base of Internet of Things (ІоТ) devices is very widespread in various applications. Intellectual analysis of the data collected by similar devices is an important task for efficient and successful functioning of such systems. In particular, the reliability of such kind of analysis has greatly influence on the ability to partially or fully automate certain processes or subsystems. However, imperfect devices of data collection, transportation errors, etc. cause data missing to appear. A number of limitations cause this problem, and in the work, they makes it impossible an effective intellectual analysis for specific use. That is why the scientific and applied problem of effectively filling the missing in the data collected by the sensors of specific characteristics should be considered. Methods: The authors propose a new prediction method for solving this problem based on the use of General Regression Neural Networks (GRNN). Results: The possibility of approximation and partial elimination of the error of computational intelligence of this type has been analytically proved. A cascade of two sequentially connected GRNN was developed. The optimal parameters of the developed cascade were selected. The simulation of its work was performed to solve the problem of recover missing sensor data in the dataset for monitoring the state of air environment. A high number of missing for one reason or another characterizes this real data set, collected by IoT device. Conclusion: High accuracy of cascade operation in comparison with existing methods of this class is inserted. All advantages and disadvantages are described. Perspectives of further research are outlined.