三种分类预测模型在医学中的应用研究  

A Study on Application of Three Prediction Models in Medicine

作　　者：何馨 邹绮蕾 卞禾 何诗思 

机构地区：[1]南方医科大学公共卫生与热带医学学院生物统计学系,广东广州510515

出　　处：《怀化学院学报》2014年第11期29-32,共4页Journal of Huaihua University

基　　金：广东省大学生创新创业训练计划项目"BP神经网络和贝叶斯神经网络的模拟比较研究及其应用"(1212113041);国家自然科学基金面上项目"X伴性遗传病印记效应检测及其关联分析的统计方法研究"(81373098);国家自然科学基金面上项目"印记基因检测及基于印记效应的疾病易感基因定位的统计方法研究"(81072386)

摘　　要：基于一个肾衰竭患者数据,应用两种神经网络(BP神经网络和贝叶斯正则化BP神经网络)与常用的二分类Logistic回归对肾衰竭患者是否死亡进行预测,并比较三种模型的预测效果.三个模型的判对率都达到89%以上.其中,以贝叶斯正则化BP神经网络的判对率和ROC曲线下面积(AUC)最大,即预测效果最好;BP神经网络和Logistic回归预测效果差别不大.Based on a real dataset, we compare two kinds of neural network（ BP Neural Network and Bayesian Regularized BP Neural Network） and Logistic Regression in medical statistics. By using SPSS 21. 0 and Matlab, after variable screening,train the three models and compare their prediction accuracy. Besides, draw their ROC curves and compare their areas under the curve（ AUC） among the three models. All of the three models have reached the prediction accuracy over 89%. Bayesian Regularized BP Neural Network has the best results with the highest prediction accuracy and the largest AUC. Unlike the researches before,in our study,BP Neural Network did not have a better performance than Logistic Regression. The small sample size may result in the BP Neural Network without a good training. However,it may also highlight an advantage of Bayesian Regularized BP Neural Network,which still gets a good output under the situation of a small sample.

关 键 词：LOGISTIC回归 神经网络 ROC曲线 分类 

分 类 号：R195.1[医药卫生—卫生统计学]