基于代价敏感主动学习算法的2型糖尿病诊断

doi:10.3969/j.issn.1006-2475.2018.06.016

摘要/Abstract

摘要： 建立2型糖尿病诊断模型，并通过主动学习解决医疗数据中标记样本较少的问题。2型糖尿病的诊断可以被看作一个代价敏感的二分类问题，本文基于逻辑回归模型、支持向量机模型和人工神经网络模型，采用基于期望误差减小的代价敏感主动学习方法，将主动学习算法和代价敏感分类算法相结合来构建诊断模型，将不同的误分类代价考虑到样本的选择中。在2型糖尿病诊断问题中，基于期望误差减小的代价敏感主动学习算法表现最优，以较少的样本标记达到了最低的误分类代价，因此主动学习算法能够减少医疗数据挖掘中需要标记的样本数，节省标注成本，同时保证模型的性能。

关键词: 糖尿病, 诊断模型, 代价敏感分类, 主动学习, 逻辑回归, 支持向量机, 人工神经网络

Abstract: Inthisstudy,adiagnosismodelfortype2diabeteswasbuiltandthelabelabsenceprobleminmedicaldatawassolvedbyactivelearning.Thediagnosisoftype2diabetescanbeseenasacost-sensitivebinaryclassificationtask.Takinglogisticregression,supportvectormachines(SVM)andartificialneuralnetwork(ANN)asthebasemodel,thisstudyadoptedthecost-sensitiveactivelearningalgorithmbasedontheexpectederrorreductionframework,whichcombinedtheactivelearningstrategywiththecost-sensitiveclassificationalgorithmandintroducedthecostinformationintotheinstancesamplingprocess.Forthediagnosisoftype2diabetes,thecost-sensitiveactivelearningalgorithmbasedontheexpectederrorreductionframeworkperformedbestinthesecomparedactivelearningstrategiesanditachievedtheminimummisclassificationcostsbylabelingfewerinstances.Activelearningalgorithmscanreducethenumberofinstancestobelabeled,savethelabelingcostsandguaranteethemodelperformanceatthesametime.

Key words: diabetes；diagnosticmodel；cost-sensitiveclassification；activelearning, logisticregression；supportvectormachine；artificialneuralnetwork

中图分类号:

TP181

许智彪. 基于代价敏感主动学习算法的2型糖尿病诊断[J]. 计算机与现代化, 2018, 0(06): 84-.

XUZhi-biao. DiagnosisofType2DiabetesBasedonCost-sensitiveActiveLearningAlgorithm[J]. Computer and Modernization, 2018, 0(06): 84-.

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