Data mining and diagnosis of heart diseases: a hybrid approach to the b-mine algorithm and association rules

Abstract

Existing systems for diagnosing heart disease are time consuming, expensive, and prone to error. In this regard, a diagnostic algorithm has been proposed for the causes of heart disease based on a frequent pattern with the B-mine algorithm optimized by association rules. Initially, a data set of disease is used to select a feature, so that it deals with a set of training features. Then, association rules are used to classify educational and experimental sets, and then the factors affecting heart disease are analyzed. The numerical results from the experiments of real and standard datasets of cardiac patients show that the average accuracy of the proposed method is approximately 98%, which has been tested on the Cleveland database that includes 76 features in the case of heart disease dataset, 14 features of which are related to heart disease. This paper also uses four common categories such as decision tree to build the model. The data set studied in this article contains 270 records as well as 14 features. The accuracy of predicting the results of the support vector machine classifications, k nearest neighbor, decision tree and simple Bayesian is 81.11%, 66.67%, 59.72% and 19.85%, respectively, which are relatively satisfactory results.

Keywords

• frequent pattern
• heart disease
• data mining
• b-mine algorithm
• association rules