Paper Title
Machine Learning Models for the Prediction of Cardiac and Non-Cardiac Deaths in Patients with Cardiovascular Disease

Abstract
Cardiovascular disease is one of the most dreadful diseases in the whole world nowadays. The objective of this paper is to predict the occurrences of cardiac and non-cardiac deaths after hospital discharge in patients with cardiovascular disease. This study determines the death types of cardiovascular patients. It ascertains whether the cardiovascular patient’s death was occurred due to cardiac arrest or any other reason other than cardiac death. For our experiments, we have examined three machine learning models such as Support Vector Machine (SVM), Random Forest and Naïve Bayes and experimented on KAMIR IV dataset which is a Korean cardiovascular patients’ dataset. The performance of all the three models was measured through accuracy, specificity, sensitivity, and F1-score. SVM showed the highest accuracy of 91%, followed by Random Forest and Naïve Bayes 67%,70% respectively. Keywords - Cardiovascular Disease, Machine Learning, Support Vector Machine, Random Forest, Naïve Bayes.