Heart Failure Severity Estimation and Pseudo‐Temporal Severity‐State Visualization From Echocardiographic Videos Using Machine Learning and Network Analysis

Heart failure, a serious cardiovascular disease that demands precise and early evaluation of its severity for guiding efficient clinical decisions. Although few classification/risk assessment approaches are based on the image data but offer limited interpretation of echocardiography images. In this paper, a machine learning approach has been developed for automated estimation of heart failure severity from the Echography images of the EchoNet‐Dynamic dataset. The deep cardiac features are extracted using the pre‐trained EfficientNet‐B0, and feature selection is achieved using the Random Forest (RF), followed by optimization of the feature space with particle swarm optimization (PSO). The optimized feature vector is fed to the eXtreme Gradient Boosting (XGBoost) regression model to predict continuous severity values, and then mapped to the clinical interpretative severity‐stages (Mild, Moderate, Severe and Critical risk). The system additionally proposes temporal visualization and the NetworkX‐based visualization of stage transitions to provide better understanding of the predicted outcome. The achieved R score is 0.9995 with MAE of 0.1157, MSE of 0.0811, and RMSE of 0.2847, which showed the effectiveness of the proposed framework to effectively estimate the heart failure severity from the echocardiographic images and provide interpretative information of severity‐stage transition in an effective and comprehensible way. The developed framework can be a promising candidate for clinical decision support system. Because the EchoNet‐Dynamic dataset provides only cross‐sectional echocardiographic measurements without repeated patient follow‐up, the temporal ordering used for visualization is a simulated (pseudo‐temporal) construct designed to aid interpretability, rather than a model of observed longitudinal predicted severity‐state relationships.