基于非平衡数据的癫痫发作预警模型研究

目的构建数据不平衡时癫痫发作频率预警模型。方法以2008年9月～2011年1月在临沂市人民医院就诊的736例癫痫患者为研究对象,进行癫痫发作危险因素的流行病学调查。采用Smote算法,进行数据的平衡化处理、后构建随机森林模型(RF),对癫痫患者的发作频率(次/月)进行分类预测。结果采用随机森林模型对癫痫患者进行分类,判断的正确率为82.53%,误判率为17.47%,受试者工作特征曲线下的面积为94.2%,袋外误差率为13.3%。结论随机森林法能够对癫痫患者发作的频率进行快速的分类,为癫痫发作的预警提供科学依据。

A predictive model of epileptic seizures based on unbalanced data

Objective To construct a predictive model of epileptic seizures based on unbalanced data.Methods The study included 736 epileptic patients treated in Linyi People′s Hospital from September 2008 to January 2011.Epidemiological investigation on risks factors for seizures was made.As the frequency of seizures in epileptic patients was non-balanced data,the data were made balanced based on the Smote Algorithm.Then the random forest was applied to construct a model to make discriminant prediction of the frequency of seizures.Results Using the random forest to analyze the data,the correctly classified accuracy was 82.53%,incorrectly classified accuracy was 17.47%,area under the receiver operating characteristic(ROC) curve was 94.2%,and out of bag error(OOB) was 13.3%.Conclusion The random forest is capable of rapidly discriminating the frequency of seizures after processing unbalanced data,which can provide a scientific basis for the forecast of seizures.