Welcome to the upgraded BRAC University Institutional Repository. We are currently organizing collections after a recent system upgrade. Homepage category counters may temporarily show lower numbers while syncing, but over 27,000 repository items remain safe and accessible. Please use the search bar to find theses, scholarly outputs, and institutional documents.

Heart disease prediction using techniques of classification in machine learning

Simple item page
bracu.degree.levelUndergraduate
bracu.type.groupStudent Works
datacite.rightsOpen Access
dc.contributor.advisorChakrabarty, Amitabha
dc.contributor.authorAfrose, Sadia
dc.contributor.authorRubaiat, Farah
dc.contributor.authorTabassum, Homayra
dc.contributor.departmentDepartment of Computer Science and Engineering
dc.date.accessioned2021-10-18T07:16:43Z
dc.date.available2021-10-18T07:16:43Z
dc.date.copyright2021
dc.date.issued2021-06
dc.descriptionCataloged from PDF version of thesis.
dc.descriptionIncludes bibliographical references (pages 42-44).
dc.descriptionThis thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.en_US
dc.description.abstractIn this thesis we have examined the accuracy of various classifiers to predict heart disease and heart vessel blockage. We have also analyzed the key features contribut ing to heart vessel blockage. We have used a dataset containing 14 attributes related to heart disease of 1025 patients. From our study we found that the Decision Tree, Random Forest and KNN algorithm gave the highest accuracy for detecting heart disease. For predicting heart vessel blockage, the Decision tree had the highest accuracy. While analyzing the features contributing to heart vessel blockage, we found that patients’ age and cholesterol level has the highest contribution. Hence, monitoring the patient’s cholesterol level may help prevent heart vessel blockage.en_US
dc.description.degreeBachelor of Science in Computer Science
dc.description.statementofresponsibilitySadia Afrose
dc.description.statementofresponsibilityFarah Rubaiat
dc.description.statementofresponsibilityHomayra Tabassum
dc.format.extent44 pages
dc.identifier.otherID 17101546
dc.identifier.otherID 17101540
dc.identifier.otherID 17301068
dc.identifier.urihttp://hdl.handle.net/10361/15348
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.subjectCoronary Artery Blocksen_US
dc.subjectChest Painen_US
dc.subjectMachine Learningen_US
dc.subjectAnginaen_US
dc.subjectDisease Predictionen_US
dc.subjectCatBoosten_US
dc.subjectXGBoosten_US
dc.subjectAdaBoosten_US
dc.subjectDecision Treeen_US
dc.subjectSVMen_US
dc.subjectKNNen_US
dc.subjectNaive Bayesen_US
dc.subjectLogistic Regressionen_US
dc.subjectLinear Regressionen_US
dc.subject.lcshMachine Learning
dc.titleHeart disease prediction using techniques of classification in machine learningen_US
dc.typeThesisen_US

Files

Original bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
17101546, 17101540, 17301068_CSE.pdf
Size:
3.8 MB
Format:
Adobe Portable Document Format
Description:
Download

License bundle

Now showing 1 - 1 of 1
Thumbnail Image
Name:
license.txt
Size:
1.71 KB
Format:
Item-specific license agreed upon to submission
Description:
Download

Collections

Thesis (Bachelor of Science in Computer Science)