What makes survival of heart failure patients? Prediction by the iterative learning approach and detailed factor analysis with the SHAP algorithm

Abstract

Cardiovascular disease is the leading cause of global death and disability. There are many types of cardiovascular diseases. The diagnosis of heart failure, one of the cardiovascular disease types, is a challenging task and plays a significant role in guiding the treatment of patients. However, machine learning approaches can be helpful for assisting medical institutions and practitioners in predicting heart failure in the early phase. This study is the first application that analyzes the dataset containing clinical records of 299 patients with heart failure using a feedforward backpropagation neural network (NN). The aim of this study is to predict the survival of heart failure patients based on the clinical data and to identify the strongest factors influencing heart failure disease development. We adopted the Shapley additive explanations (SHAP) values, which have been used to interpret model findings. From the study, it is observed that the best and highest accuracy of 91.11% is obtained compared to previous studies and it is found that feedforward backpropagation NN performed better than the previous approaches. Also, this study revealed that time, ejection fraction (EF), serum creatinine, creatinine phosphokinase (CPK), and age are the strongest risk factors for mortality among patients suffering from heart failure.