A Mortality Predicting Model for Heart Failure Patients Based on AdaBoost with Multi-kernel SVM

Abstract

Purposes Heart failure is a complex clinical syndrome with significant features such as high morbidity, high mortality, and poor prognosis. It is the terminal stage in the development of all types of heart disease and seriously threatens human health. Therefore, early prognostic assessment studies of heart failure patients are crucial to help the survival of patients. Methods A heart failure mortality assessment model (MK-SVM-AdaBoost) based on Multi Kernel Support Vector Machine (MK-SVM) and Adaptive Boosting (AdaBoost) algorithm is proposed. The algorithm utilizes MK-SVM to map features into a high-dimensional space and integrates basic classifiers on the basis of the AdaBoost algorithm to achieve accurate mortality prediction. Meanwhile, a hybrid sampling method combining Synthetic Minority Oversampling Technique (SMOTE) and Tomek links under-sampling technique is introduced into the prediction model to alleviate the impact of unbalanced datasets on model performance. Findings Experiments were performed on a small heart failure dataset collected from Bethune Hospital for mortality prediction in heart failure patients within 30 days. The experimental results show that the accuracy and recall of the MK-SVM-AdaBoost model reach 85.63% and 86.33%, respectively, which are better than thase of the existing methods. The Area Under Curve (AUC) under the ROC curve enclosed with the axes and its micro-mean (MiA-AUC) reach 91.00% and 92.00%, respectively, which indicates that the proposed model has good stability. Conclusions The proposed model has high accuracy and stability, and can provide some reference for the clinical decision-making of doctors. In the future, the dataset will be expaned and the graded warnings will be studied for more effective assessment of patients.

Keywords

• heart failure
• multi-kernel support vector machine
• adaboost algorithm
• mortality prediction