Data mining is process to analyses number of data sets and then extracts the meaning of data. It helps to predict the patterns and future trends, allowing business in decision making. Data mining applications are able to give the answer of business questions which can take much time to resolve traditionally. High amount of data that can be generated for the prediction of disease is analyzed traditionally and is too complicated along with voluminous to be processed. Data mining provides methods and techniques for transformation of the data into useful information for decision making. These techniques can make process fast and take less time to predict the heart disease with more accuracy. The healthcare sector assembles enormous quantity of healthcare data which cannot be mined to uncover hidden information for effectual decision making. However, there is a plenty of hidden information in this data which is untapped and not being used appropriately for predictions. It becomes more influential in case of heart disease that is considered as the predominant reason behind death all over the world. In medical field, Data Mining provides several methods which are widely used in the medical and clinical decision support systems which should be helpful for diagnosis and predicting of various diseases. These data mining techniques can be used in heart diseases takes less time and make the process much faster for the prediction system to predict diseases with good accuracy to improve their health. In this paper we survey different papers in which one or more algorithms of data mining used for the prediction of heart disease. By Applying data mining techniques to heart disease data which requires to be processed, we can get effective results and achieve reliable performance which will help in decision making in healthcare industry. It will help the medical practitioners to diagnose the disease in less time and predict probable complications well in advance. Identify the major risk factors of Heart Disease categorizing the risk factors in an order which causes damages to the heart such as diabetes, high blood cholesterol, obesity, hyper tension, smoking, poor diet, stress, etc. Data mining techniques and functions are used to identify the level of risk factors which helps the patients to take precautions in advance to save their life.