I. Introduction
Heart diseases are a class of life threatening diseases that have become the leading cause of mortality in the past decade [1]. World Health Organization (WHO) has estimated 17.9 million deaths worldwide each year due to cardiovascular diseases. United Nations Sustainable Development Goals aim to diminish the premature mortality by a one-third till year 2030 from non-communicable diseases [2]. Heart disease prediction is a pertinent research area that has gained the attention of various data mining researchers. Data mining techniques can be applied for the prediction of heart diseases which will lead to lowering of premature mortality rate as appropriate treatment can be provided to the diseased individuals in a timely manner. Machine learning is a part of data mining techniques that can uncover the hidden trends by finding differences in diseased and healthy people. These techniques utilize the historically labelled data for learning a model which can later be used for making predictions corresponding to new instances. Data mining techniques have shown good efficiency in various applications such as recommendation systems, Netflix movie rating prediction, bot learning for self driving cars, etc. It is also being used for various prediction tasks in healthcare industry [3] [4].