Prediction of Cardiovascular Risk Analysis and Performance Evaluation Using Various Data Mining Techniques:A Review

In the recent trends of technology implementation, the knowledge discovery in database (KDD) is alarmed with development of methods and techniques for making use of data. This can be achieved by one of the most important step of the KDD:Data Mining. Data mining is the process of pattern discovery and extraction where huge amount of data is involved i.e., process of analyzing enormous sets of data and then extracting the meaning of the data. Both the data mining and healthcare industry have emerged some reliable early detection systems and other various healthcare related systems from the clinical, and diagnosis data. The data generated from this prediction for the heart disease are complex and voluminous to be processed and very difficult to be analyzed using some of the existing traditional methods. The techniques and methodologies available in data mining help to transform this huge amount of data into specific and useful data for decision making. These data mining techniques consume less time for the prediction of the disease with more accuracy, to achieve the same. In this paper we have reviewed various paper involved in terms of algorithms, methodologies used, and results in this field. Results and evaluation methods are discussed and a summary of the finding is presented to conclude the paper. Applying data mining techniques to heart disease data can provide as reliable performance as that achieved in diagnosing heart disease.