DiagnosisofType2DiabetesBasedonCost-sensitiveActiveLearningAlgorithm

doi:10.3969/j.issn.1006-2475.2018.06.016

Abstract

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

CLC Number:

TP181

XUZhi-biao. DiagnosisofType2DiabetesBasedonCost-sensitiveActiveLearningAlgorithm[J]. Computer and Modernization, doi: 10.3969/j.issn.1006-2475.2018.06.016.

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