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Predicting The Risk of Heart Disease Using Advanced Machine Learning Approach


Rakhi Wajgi, Sonali Choudhary, Punit Fulzele
Abstract

HeartdiseasesalsocalledCardiovascularDiseases(CVD)includerangeofconditionsportrayingillnessofheart.Theseincludediseasesrelatedtobloodvessels,rhythmproblem,chestpain,heartattack,strokes,andfluctuatingbloodpressure.PersonsufferingwithCVDhasfluctuatingbloodflowrate.CVDaretheleadingcauseofmortalityinIndiaincludingbothmaleandfemale.Aquarterofallmortalityisattributedtocardiovasculardiseases.Heartdiseasesandstrokesarethepredominantcausesandareresponsiblefor>80%ofCVDdeaths.Thereforeinthispaperamachinelearningmodelisimplementedonthedatasetdownloadedfromkaggle.Thisdatasetcontainsvariousparameterscontributingtocardiacmorbidity.Itcontains70000recordsandcontainsparameterslikeage,cholesterol,glucose,smoking,alcoholichabitetc.ThedecisionTreemodelisusedfottrainingandpredictingtherisk ofheart disease.Theaccuracyof implemented model is 73%.

Volume 11 | Issue 7

Pages: 1030-1037