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Cardiovascular Disease Risk Prediction Using Data Mining Techniques

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dc.contributor.author Ahamed, Istyak
dc.contributor.author Hossain, Shohanur
dc.date.accessioned 2020-11-28T07:33:01Z
dc.date.available 2020-11-28T07:33:01Z
dc.date.issued 2019-12-05
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/5164
dc.description.abstract At present, cardiovascular disease has become the leading cause of death worldwide. Particularly in the South Asian countries have a tremendous risk of cardiovascular disease at an early age than any other ethnic group. Most often it’s challenging for medical practitioners to predict cardiovascular disease as it requires experience and knowledge which is a complex task to accomplish. This health industry has enormous amounts of data which is useful for making effective conclusions using their hidden information. Using appropriate results and making effective decisions on data, some superior data mining techniques are used such as Logistic Regression, Decision Tree, Nave Bayes , SVM. By using some properties like (age, gender, bp, stress etc) we can be predicted the chances of cardiovascular disease. en_US
dc.language.iso en en_US
dc.publisher Daffodil International University en_US
dc.subject Data Mining en_US
dc.subject Database Management en_US
dc.title Cardiovascular Disease Risk Prediction Using Data Mining Techniques en_US
dc.type Other en_US

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