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dc.contributor.advisorArif, Hossain
dc.contributor.authorAli, Musfiq
dc.contributor.authorKhan, MD. Iftiyar
dc.contributor.authorImran, Masud Al
dc.contributor.authorSiddiki, Musnath
dc.date.accessioned2019-06-27T11:06:06Z
dc.date.available2019-06-27T11:06:06Z
dc.date.copyright2019
dc.date.issued2019-04
dc.identifier.otherID 14101220
dc.identifier.otherID 14101246
dc.identifier.otherID 14301018
dc.identifier.otherID 1221048
dc.identifier.urihttp://hdl.handle.net/10361/12269
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (page 28).
dc.description.abstractAccording to recent survey by WHO (World health organization) 17.9 million people die each year because of heart related diseases and it is increasing rapidly. With the increasing population and disease, it is become a challenge to diagnosing disease and providing the appropriate treatment at the right time. But there is a light of hope that recent advances in technology have accelerated the public health sector by developing advanced functional biomedical solutions. This paper aims at analyzing the various data mining techniques namely Naive Bayes, Random Forest Classification, Decision tree and Support Vector Machine by using a qualified dataset for Heart disease prediction which is consist of various attributes like gender, age, chest pain type, blood pressure, blood sugar etc. The research includes finding the correlations between the various attributes of the dataset by utilizing the standard data mining techniques and hence using the attributes suitably to predict the chances of a heart disease. These machine learning techniques take less time for the prediction of the disease with more accuracy which will reduce the dispose of valuable lives all over the world.en_US
dc.description.statementofresponsibilityMD. Musfiq Ali
dc.description.statementofresponsibilityMD. Iftiyar Khan
dc.description.statementofresponsibilityMasud Al Imran
dc.description.statementofresponsibilityMusnath Siddiki
dc.format.extent28 pages
dc.language.isoenen_US
dc.publisherBRAC Universityen_US
dc.rightsBrac University theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission.
dc.subjectHeart diseaseen_US
dc.subjectMachine learningen_US
dc.subject.lcshMachine learning.
dc.titleHeart disease prediction using machine learning algorithmsen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science and Engineering


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