Arif, HossainAli, MusfiqKhan, MD. IftiyarImran, Masud AlSiddiki, Musnath2019-06-272019-06-2720192019-04ID 14101220ID 14101246ID 14301018ID 1221048http://hdl.handle.net/10361/12269Cataloged from PDF version of thesis.Includes bibliographical references (page 28).This thesis is submitted in partial fulfillment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2019.According 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.28 pagesenBrac 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.Heart diseaseMachine learningMachine learning.Thesis