Comparative Study of Data Mining Techniques on Heart Disease Prediction System: a case study for the

Abstract

Nowadays, the healthcare sector is one of the areas where huge data are daily generated. However, most of the generated data are not properly exploited. Important encapsulated data are currently in the data sets. Therefore, the encapsulated data can be analyzed and put into useful data. Data mining is a very challenging task for the researchers to make diseases prediction from the huge medical databases. To succeed in dealing with this issue, researchers apply data mining techniques such as classification, clustering, association rules and so on. The main objective of this research is to predict heart diseases by the use of classification algorithms namely Naïve Bayes and Support Vector Machine in order to compare them on the basis of the performance factors i.e. probabilities and classification accuracy. In this paper, we also developed a computer-based clinical Decision support system that can assist medical professionals to predict heart disease status based on the clinical data of the patients using Naïve Bayes Algorithm. It is a web-based user-friendly system implemented on ASP.NET platform with C# and python for the data analysis. From the experimental results, it is observed that the performance of Naïve Bayes is better than the other Algorithm.