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Survival Analysis of Heart Failure Patients Using Machine Learning

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dc.contributor.author Huda, S.M Rakibul
dc.contributor.author Hasan, Nabid
dc.contributor.author Ahsan, Fahad
dc.date.accessioned 2022-07-30T05:57:39Z
dc.date.available 2022-07-30T05:57:39Z
dc.date.issued 2022-01-13
dc.identifier.uri http://dspace.daffodilvarsity.edu.bd:8080/handle/123456789/8367
dc.description.abstract In modern days heart attack has become a major disease all over the worldwhich is causing huge number of death. Heart attack at early age is a greater risk factor for the people of south Asia than the other regions. It is really hard to predict the stage of a heart attack patient quickly and successfully, because it need a long lime experience and passionate knowledge. Medical industries has a large amount of data which can be used to make effective decision with all the concealed information. With the help of effective decision making and few excellent data mining technique like Logistic Regression, Decision Tree, we will able to predict heart attack patients situation or stage quickly. We used four algorithm in our system those are Random forest classifier, Decision tree, Logistic regression, Support vector machine. Our final model accuracy is 92% where the algorithm is Support Vector Machine(SVM). en_US
dc.language.iso en_US en_US
dc.publisher ©Daffodil International University en_US
dc.subject Machine learning en_US
dc.subject Heart failure en_US
dc.subject Heart attack en_US
dc.title Survival Analysis of Heart Failure Patients Using Machine Learning en_US
dc.type Other en_US


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