Arif, HossainHabib, SumayaMoin, Maisha BinteAziz, Sujana2019-02-242019-02-2420182018-12ID 15101129ID 15201003ID 15101019http://hdl.handle.net/10361/11446Cataloged from PDF version of thesis.Includes bibliographical references (pages 25-27).This thesis is submitted in partial fulfilment of the requirements for the degree of Bachelor of Science in Computer Science and Engineering, 2018.With the ever increasing population of the world, diseases and their possibilities are also increasing at an alarming rate. As time passes by, diagnosing diseases and providing appropriate treatment at the right time has become quite a challenge. Heart diseases, for one, have been a major cause of death worldwide. Therefore, this research has been focused on finding an efficient way to predict the chances of a heart failure and accordingly, recommend appropriate medicines to aid cardiologists in quicker decision making. The research includes finding the correlations or associations between the various attributes of the dataset by utilizing the standard techniques of exploratory analysis and hence using the attributes suitably to predict the chances of a heart failure, as well as the medicine recommendations. A comparative study has also been included which shows the various attained accuracy rates of different machine learning algorithms including - Logistic Regression, Naïve Bayes, Decision Tree, Linear SVC, Random Forest, and Gradient Boosting Classifier. The Apache Spark framework has been used in order to make the system capable of handling big data.27 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.Risk predictionExploratory analysisBig data analyticsHeart diseaseData miningBig dataQuantitative researchThesis