Heart disease prediction using techniques of classification in machine learning

This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.

Bibliografiske detaljer
Main Authors:	Afrose, Sadia, Rubaiat, Farah, Tabassum, Homayra
Andre forfattere:	Chakrabarty, Amitabha
Format:	Thesis
Sprog:	English
Udgivet:	Brac University 2021
Fag:	Heart Disease Coronary Artery Blocks Chest Pain Machine Learning Angina Disease Prediction CatBoost XGBoost AdaBoost Decision Tree SVM KNN Naive Bayes Logistic Regression Linear Regression
Online adgang:	http://hdl.handle.net/10361/15348

id	10361-15348
record_format	dspace
spelling	10361-153482022-01-26T10:08:15Z Heart disease prediction using techniques of classification in machine learning Afrose, Sadia Rubaiat, Farah Tabassum, Homayra Chakrabarty, Amitabha Department of Computer Science and Engineering, Brac University Heart Disease Coronary Artery Blocks Chest Pain Machine Learning Angina Disease Prediction CatBoost XGBoost AdaBoost Decision Tree SVM KNN Naive Bayes Logistic Regression Linear Regression Machine Learning This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021. Cataloged from PDF version of thesis. Includes bibliographical references (pages 42-44). In 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. Sadia Afrose Farah Rubaiat Homayra Tabassum B. Computer Science 2021-10-18T07:16:43Z 2021-10-18T07:16:43Z 2021 2021-06 Thesis ID 17101546 ID 17101540 ID 17301068 http://hdl.handle.net/10361/15348 en Brac 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. 44 pages application/pdf Brac University
institution	Brac University
collection	Institutional Repository
language	English
topic	Heart Disease Coronary Artery Blocks Chest Pain Machine Learning Angina Disease Prediction CatBoost XGBoost AdaBoost Decision Tree SVM KNN Naive Bayes Logistic Regression Linear Regression Machine Learning
spellingShingle	Heart Disease Coronary Artery Blocks Chest Pain Machine Learning Angina Disease Prediction CatBoost XGBoost AdaBoost Decision Tree SVM KNN Naive Bayes Logistic Regression Linear Regression Machine Learning Afrose, Sadia Rubaiat, Farah Tabassum, Homayra Heart disease prediction using techniques of classification in machine learning
description	This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2021.
author2	Chakrabarty, Amitabha
author_facet	Chakrabarty, Amitabha Afrose, Sadia Rubaiat, Farah Tabassum, Homayra
format	Thesis
author	Afrose, Sadia Rubaiat, Farah Tabassum, Homayra
author_sort	Afrose, Sadia
title	Heart disease prediction using techniques of classification in machine learning
title_short	Heart disease prediction using techniques of classification in machine learning
title_full	Heart disease prediction using techniques of classification in machine learning
title_fullStr	Heart disease prediction using techniques of classification in machine learning
title_full_unstemmed	Heart disease prediction using techniques of classification in machine learning
title_sort	heart disease prediction using techniques of classification in machine learning
publisher	Brac University
publishDate	2021
url	http://hdl.handle.net/10361/15348
work_keys_str_mv	AT afrosesadia heartdiseasepredictionusingtechniquesofclassificationinmachinelearning AT rubaiatfarah heartdiseasepredictionusingtechniquesofclassificationinmachinelearning AT tabassumhomayra heartdiseasepredictionusingtechniquesofclassificationinmachinelearning
_version_	1814307270068011008

Heart disease prediction using techniques of classification in machine learning

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