Prediction of Heart Disease Using Random Forest and Rough Set Based Feature Selection

Dataisgeneratedbythemedical industry.Oftenthisdata isofverycomplexnature—electronic records,handwrittenscripts,etc.—sinceitisgeneratedfrommultiplesources.DuetotheComplexity andsheervolumeofthisdatanecessitatestechniquesthatcanextractinsightfromthisdatainaquick andefficientway.Theseinsightsnotonlydiagnosethediseasesbutalsopredictandcanprevent disease.Onesuchuseofthesetechniquesiscardiovasculardiseases.Heartdiseaseorcoronaryartery disease(CAD)isoneofthemajorcausesofdeathallovertheworld.Comprehensiveresearchusing singledataminingtechniqueshavenotresultedinanacceptableaccuracy.Furtherresearchisbeing carriedoutontheeffectivenessofhybridizingmorethanonetechniqueforincreasingaccuracyin thediagnosisofheartdisease.Inthisarticle,theauthorsworkedonheartstalogdatasetcollected fromtheUCIrepository,usedtheRandomForestalgorithmandFeatureSelectionusingroughsets toaccuratelypredicttheoccurrenceofheartdisease KeywoRDS Classification, Feature Selection, Heart Disease, Random Forest, Rough Set

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