Heart is the key organ of our body as blood circulation towards other organs depends upon efficient working of the heart . Nowadays, Coronary artery diseases diminish the working ability of hearts to a large extent, resulting in failure of hearts in many cases. A survey conducted by WHO reveals that around 29.20% of the world’s population i.e 17 million people die due to various heart diseases each year. For identifying various heart diseases, several pathological procedures and medical investigations are being done by doctors. With the use of data mining and machine learning techniques, better insights can be provided from the existing test results and the number of pathological procedures can be reduced. A system created using Data Mining and Machine Learning algorithms, can overcome the dearth of examining tools for classifying the data and predicting the Risk state of Cardiac patients. In this paper, a comparative survey of such approaches for investigation of Cardiac diseases using Data Mining techniques is presented. These comparative study results would be really helpful for researchers in this domain for channelizing their research in the appropriate direction

Keywords : Comparative Study, Machine Learning, Investigation, Naive Bayes, K-Nearest Neighbor, Random Forest, Decision Table, K-Means