Prediction of Surgery Control Parameters in Cardiology to Optimize the Emission Fraction Values with the Help of Neural Networks

Abstract

In the Big Data era, decision tree methods, machine learning, and neural networks, along with other Data Mining methods became an alternative to classical statistical methods as a more useful tool for analyzing large and inhomogeneous data. Neural Networks methods have emerged as a more accurate and effective technology in a wide range of medical problems such as diagnosis, prediction, treatment.The purpose of the paper is to indentificate the control parameters of the surgical intervention to optimize the EF ejection fraction after the surgery using a Data Mining method (neural network) models.Results. The analysis of changes in hemodynamic parameters of children with severe heart defects due to surgery -implantation of conduit. Changes in these parameters after surgery were analyzed using analysis of variance for repeated measurements (RepANOVA).