Abstract: This paper constructs a mortality prediction system that predicts mortality within 30 day after hospitalization in heart failure patients. Data are collected from the Shanghai Shuguang hospital. It preprocesses the data and feature of heart failure patients. A traditional feature selection algorithm called Relief is utilized to select important feature because of redundancy of feature. Finally, this system adopts biased penalties-SVM（bp-SVM） algorithm to predict mortality for heart failure patients. The experimental results show that system can achieve high performance in 30-day mortality prediction. Mortality prediction system aims to assist doctors in treating heart failure patients by providing important decision information. Doctors can apply individualized treatment according to different condition of patients in order to improve the result of clinical diagnosis and the resource allocation.

Key words: feature selection, biased penalties-Support Vector Machine（bp-SVM）, mortality prediction, heart failure, machine learning

摘要： 提出心衰死亡率预测系统，预测心衰病人本次住院后30天内死亡率。基于上海曙光医院提供的心衰病人信息，首先对原始数据和特征进行预处理。由于特征的冗余性，再选用经典的Relief特征选择算法筛选出重要的心衰特征，最后选用bp-SVM算法来实现死亡率预测。实验结果证明，死亡率预测系统可以达到较高的性能并通过提供决策信息，辅助医生治疗病人。医生可以根据系统预测的病人死亡率的高低，采取不同的治疗方式，提高临床诊断结果和医院的资源分配。

关键词: 特征选择, bp-SVM, 死亡率预测, 心衰, 机器学习