带缺失数据的肝炎预后判别分析及R软件实现

Discriminant Analysis of Hepatitis Prognosis with Missing Data and Implementation of R Software

在医疗诊断中,处理分析的病例数据常常面对两个问题:缺失数据和方法选择。基于肝炎诱发肝功能衰竭的多项指标,使用机器学习模型对肝炎预后死亡进行判别分析,首先选用随机森林插补法对缺失数据进行插补,并用t检验对比均值插补与随机森林插补法的优劣,其次使用R软件将机器学习模型中决策树、Boosting模型、随机森林等六种模型运用到同一肝炎数据集的死亡判别,并通过K折交叉验证比较各机器学习模型的判别精准度,最终结果表明:Boosting模型判别精准度最高达100%且模型最稳定。

In medical diagnosis, processing analytical data often faces two problems: missing data and method selection. In this paper, discriminant analysis of hepatitis prognosis death using machine learning model based on multiple indicators of hepatitis-induced liver failure. Firstly, the Random Forest interpolation method is used to interpolate the missing data, and the t test is used to compare the advantages and disadvantages of Mean-interpolation and Random Forest-interpolation. Secondly, the R software is used to apply the six models of Decision tree model, Boosting model, Random Forest model and so on to the death judgment of the same hepatitis data set. Finally, the accuracy of each machine learning model is compared by K-fold cross validation. The final result shows that the Boosting model has a discriminative accuracy of up to 100% and the model is the most stable.