Generation of Synthetical Medical Data by MDR-Analysis

Abstract

Purpose: The purpose of this article is to outline an algorithm for generating synthetic medical data in order to augment small samples of data. Methods: To achieve the research goal, methods such as: correlation analysis (to identify significant variables and the relationships between them), MDR analysis (to build logical chains of relationships between medical data), and regression analysis (to model medical data variables to use this to generate synthetic data) were used. Results: A database of heart failure patients that is publicly available was used to test the developed algorithm for generating synthetic medical data in action; as a result, statistical relationships between data were found and used to build linear regression models. Discussion: The proposed algorithm allows, with a few simple, yet important actions, to perform the generation of medical data, which makes it possible to obtain large data sets that can be used to implement machine learning methods in any tasks related to medicine.