Heart failure risk prediction and medicine recommendation using exploratory data analysis

Abstract

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 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 medical profiles of the patients 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 Support Vector Classifier, Random Forest, and Gradient Boosting Classifier. The Apache Spark framework has been used in order to make the system capable of handling big data.