Heart Disease Prediction with PCA and SRP

Abstract

In this expeditiously modern world, it all depends on how effectively the data can be maintained and utilized for a suitable purpose. Handling such a large quantity of dynamic data is not at all an easy task. On the contrary, we can use classification techniques, which is purely for building the relationships among huge databases by easily predicting the outcomes by considering the type of relationship. This kind of techniques plays an essential role in every aspect of science and engineering, for example, human services, education, web-based businesses. In the Health maintenance industry, all the data mining techniques are most part utilized for malady prediction. The main goal in this work attempts deeply to anticipate the occurrence of coronary disease with reduced attributes in the dataset. In this case, 14 characteristics are associated with anticipating coronary illness. Following the process, Five classifiers like Classification by clustering, Support Vector Machine, Naive Bayes, Random Forest, Decision Tree are utilized to anticipate the diagnostic influence of heart disease once after reducing the range of characteristics.