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dc.contributor.advisorChoudhury, Najeefa Nikhat
dc.contributor.advisorHawlader, Ahanaf Hassan
dc.contributor.authorSalam, Nazia Binte
dc.contributor.authorRaisa, Samiha
dc.contributor.authorRashid, Rahela Atia
dc.contributor.authorNoor, Asmita
dc.contributor.authorObaed, Sin-Sumbil Binte
dc.date.accessioned2023-07-10T06:10:32Z
dc.date.available2023-07-10T06:10:32Z
dc.date.copyright2022
dc.date.issued2022-05
dc.identifier.issnID 18301080
dc.identifier.otherID 18301156
dc.identifier.otherID 18301150
dc.identifier.otherID 19101640
dc.identifier.otherID 18301092
dc.identifier.urihttp://hdl.handle.net/10361/18702
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science, 2022.en_US
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 31-33).
dc.description.abstractCardiovascular disease is a leading cause of death worldwide. According to the Centers for Disease Control and Prevention, one person dies from heart disease every 36 seconds in the United States. In 2019, an estimated 17.9 million people died from CVD worldwide. High blood pressure, an unhealthy diet, high cholesterol, diabetes, air pollution, obesity, tobacco use, kidney disease, physical inactivity, harmful alcohol use, and stress can all contribute to it. Family history, ethnic background, sex, and age are some other contributing factors to a person’s risk of heart disease. This paper seeks to predict heart diseases using a dataset that has factors like age, sex, the number of cigarettes smoked, etc. This prediction will be done by analyzing different parameters like blood pressure, oxygen level, hemoglobin count, etc. which are the major deciding factors to measure heart risks. The research will use supervised Machine Learning (ML) algorithms such as decision tree (a classification algorithm that works on categorical as well as numerical data), K-Nearest Neighbor (K-NN), Random forest algorithm, etc. to provide an accurate prediction. After applying ML on medical data, the outcome will be used to conduct a comparative analysis to measure the efficiency of different ML algorithms in predicting cardiovascular diseases. Furthermore, the major objective of this research is to use the algorithms and process in Bangladeshi dataset and explore the result outcome and newer possibilities.en_US
dc.description.statementofresponsibilityNazia Binte Salam
dc.description.statementofresponsibilitySamiha Raisa
dc.description.statementofresponsibilityRahela Atia Rashid
dc.description.statementofresponsibilityAsmita Noor
dc.description.statementofresponsibilitySin-Sumbil Binte Obaed
dc.format.extent33 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.subjectCardiovascular diseaseen_US
dc.subjectRandom forest algorithmen_US
dc.subjectK-NNen_US
dc.subject.lcshMachine learning
dc.subject.lcshComputer algorithms
dc.titlePrediction of coronary heart diseases using supervised machine learning algorithmsen_US
dc.typeThesisen_US
dc.contributor.departmentDepartment of Computer Science and Engineering, Brac University
dc.description.degreeB. Computer Science


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