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
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