A GRNN-based Approach towards Prediction from Small Datasets in Medical Application
Ivan Izonin, Roman Tkachenko, Michal Greguš, Khrystyna Zub, Pavlo Tkachenko
Abstract
A small data approach is an important area of research in the medical field. Many tasks in this area require the development of such direction due to the high cost of obtaining dataset or the inability to obtain sufficient observations for the analysis. Intelligent analysis of small datasets, which are very common in this area, will improve the quality of diagnosis, prevention or treatment. In this paper, the authors developed a new method of handling small datasets. The method is based on the use of a GRNN. The main steps of algorithms for preparation and application of the developed input doubling method are described. The modelling of the method is carried out; the optimal parameters for its functioning are selected. The efficiency of the method is established using a short set of clinical data by comparing its accuracy with a number of existing methods. It is shown that the developed input doubling method based on GRNN demonstrates the highest accuracy based on RMSE and MAE among all the methods considered. In addition, it provides unambiguous solutions, which avoids the use of such an approach as the "multiple runs" to choose the best solution. The main disadvantages of the method are analyzed, among which its considerable time delays of functioning in comparison with the basic GRNN that should be noted.