Predictive Modeling of Echocardiographic Parameters Using Electrocardiogram Features via Machine Learning Methods as a Tool for Assessing of Functional Status of Military Personnel

This study explores the feasibility of predicting echocardiographic (EchoCG) parameters from electrocardiogram (ECG) data using machine learning techniques. Two modeling approaches are investigated: regression for continuous parameter prediction and multi-class classification for clinically significant parameter ranges. A dataset of 37 patients with matched ECG and EchoCG data is used. Strong correlations between selected parameter pairs are identified. Results demonstrate that ensemble models such as Random Forest outperform linear models in most prediction tasks. Limitations due to data imbalance and potential improvements using balancing techniques are also discussed.