Performance Evaluation of Machine Learning Methods for Heart Failure Prediction

Abstract

Heart failure is a syndrome of cardiac circulation disorder. Due to the dysfunction of the systolic function or diastolic function of the heart, the venous blood volume cannot be fully discharged from the heart, resulting in blood stasis in the venous system and insufficient perfusion in the arterial system. The symptoms of this disorder are concentrated in pulmonary congestion and vena cava congestion. The correlation between the inducement of heart failure and the incidence of heart failure is a subject that needs to be studied in the medical field. In recent years, with the development of data mining technology, more and more analytical models and algorithms have been applied in the medical field, which greatly improve the efficiency of medical data analysis and enable medical workers to cure diseases better. In this study, an ensemble learning model is applied to analyze the data of heart failure. First, the data is preprocessed and normalized, and features that are not associated with death rate of heart failure are removed. Secondly, multiple base classifiers are trained and compared. Finally, the competent base classifiers are selected and integrated with the Stacking-based ensemble learning algorithm for final classification. Comparative analysis showed that the prediction results of ensemble model are better than that of base classifiers in evaluation indexes such as accuracy, precision, AUC, Balanced accuracy and F1-score for the heart failure data.