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We study the way dynamics affects modelling in chronic heart failure (CHF) tasks. By dynamics we understand the patient history and the appearance of new events, states and variables changing in time. The goal is to understand what impact past data has on prediction quality. Three different experiments have been conducted: CHF episode results prediction (better, worse, no change), CHF stage classification and heart rate value prediction. For modelling we use clinical data of CHF patients. For each task the groups of static and dynamic features are selected and analyzed. For each task 3 machine learning algorithms were trained: XGBoost, Logistic Regression, and Random Forest for multi classification and Linear Regression, Decision Tree and XGBoost for the regression task. Different combinations of features were examined from both groups applying forward feature selection algorithm. The results confirm that the highest predictions quality is reached with combination of static and dynamic features.

Dynamic Features Impact on the Quality of Chronic Heart Failure Predictive Modelling.