Selecting the appropriate size of the graph for self-diagnostic model with graph density

Sutat Gammanee, Sunantha Sodsee

Abstract


Self-diagnosis is the concept of self-diagnosing disease from symptomsWehavetheideatocreateself-diagnosticmodelsfromdiagnosticdata.
Thedatatobeanalyzedwerefromamedium-sizedhospitalinThailand.
Themodelisdividedbystructureddataandunstructureddata. Thefirststepistoprocessstructureddatawithclusteralgorithms. Thesecondstepistoevaluatetheunstructureddatatogroupsymptomsintoabipartitegraph. Afterthegraphwascreated,themodelwasdividedinto10levels,accordingtothelevelofsimilarity. Thisresearchaimstoapplytheconceptofdensitygraph,theKappasandmultiplelinegraphtoselectingtheappropriatediagnosismodel. Theresultsofallthreeexperimentsshowedthattheappropriatemodelwasatalevelofsimilarityat40%.Wehavetheideatocreateself-diagnosticmodelsfromdiagnosticdata.
Thedatatobeanalyzedwerefromamedium-sizedhospitalinThailand.
Themodelisdividedbystructureddataandunstructureddata. Thefirststepistoprocessstructureddatawithclusteralgorithms. Thesecondstepistoevaluatetheunstructureddatatogroupsymptomsintoabipartitegraph. Afterthegraphwascreated,themodelwasdividedinto10levels,accordingtothelevelofsimilarity. Thisresearchaimstoapplytheconceptofdensitygraph,theKappasandmultiplelinegraphtoselectingtheappropriatediagnosismodel. Theresultsofallthreeexperimentsshowedthattheappropriatemodelwasatalevelofsimilarityat40

Keywords


Bipatite graph Graph density; Machine learning;

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DOI: http://doi.org/10.11591/ijeecs.v26.i3.pp1556-1563

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