A Survey on Heart Disease Prediction System Using Data Mining Techniques

paper intends to provide a survey of current techniques of knowledge discovery in databases using data mining techniques which will be useful for medical practitioners to take effective decision. The objective of this research work is to predict more accurately the presence of heart disease with reduced number of attributes. Four classifiers like Naive Bayes, Neural network, WAC algorithms are used to predict the diagnosis of patients, whereas WAC provides the accurate result when compared to other algorithms. Data mining used to gather, store, analyze and integrate biological information which can then be used for discovery and development. Medical Data mining in healthcare is regarded as an important yet complicated task that needs to be executed accurately and efficiently. Healthcare data mining attempts to solve real world health problems in diagnosis and treatment of diseases. This survey paper aims to analyze the several data mining techniques proposed in recent years for the diagnosis of heart disease. Many researchers used data mining techniques in the diagnosis of diseases such as tuberculosis, diabetes, cancer and heart disease, in which several data mining techniques are used in the diagnosis of heart disease such as KNN, Neural Networks, and Bayesian classification. Classification based on clustering, Decision Tree, Genetic Algorithm, Naive Bayes, Decision tree, WAC which are showing accuracy at different levels. Using medical profile such as age, sex, blood pressure and blood sugar we can easily predict the likelihood of patients getting heart disease. In this paper we have evaluated the performance of new classification approach that uses the experienced Doctor's knowledge to assign the weight to each attribute. More weight is assigned to the attribute having high impact on disease prediction.