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Minimizing Symptom-based Diagnostic Errors Using Weighted Input Variables and Fuzzy Logic Rules in Clinical Decision Support Systems

Journal: International Journal of Advanced Trends in Computer Science and Engineering (IJATCSE) (Vol.10, No. 3)

Publication Date:

Authors : ;

Page : 1567-1575

Keywords : ;

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Abstract

ThisstudycoversanapproachtowardsminimizingDiseasediagnosticerrorsusingweightedinputvariablesandFuzzyLogicruleswithmultiphasediagnosticengine.Theweightswereappliedbecausedifferentsymptomsmayhavedifferentdegreesofimportanceindifferentdiseases.Thisistoensurethatrecommendationsfordiseaseconfirmationbasedonsymptomsreturngoodpercentageoftruepositiveandtruenegatives.Thestudycreatesanenhanced,accurateandprecisesystemformedicaldiagnosisevenwhenonlythesymptomsareconsidered.Inordertoevaluatethemodel,fourcategoriesofdiagnoseswerecarriedoutwithoutusingthemodelatthefirstinstanceandusingthemodelatthesecondinstancewith50patientsdoneat4differentdiagnosticinstances.Thetruepositive(TP)andtheFalsenegativestatisticswereobtainedfromwherethefalsepositiverate(TPR)orsensitivityandfalsepositiverate(FPR)werederived.ThegraphofTPRvsFPRwasplottedfromwherethequalityofdiagnosescouldbegottenfromtheReceiverOperatingCharacteristics(ROC)space.Theresultshowsthatsensitivity,whichistheabilityofatesttocorrectlyidentifythosewiththediseaseorTruePositiveRate,andspecificity,whichistheabilityofthetesttocorrectlyidentifythosewithoutthediseasealsocalledTrueNegativeRateTNRstoodat87%and86%respectivelyusingthedevelopedmodelandthesameparameteryielded72%and56%respectivelywithoutusingthemodel.Theresultalsoshowsthatthefalsepositiverate(FPR)whichindicatesthedegreeoffalsealarmis19%usingthenewmodelwhileitis44%withoutusingthemodel.Thisresultshowsthatthelikelihoodofmakingwrongclinicaldiagnosticdecisionsismuchlowerwiththisapproach

Last modified: 2021-06-11 18:42:26