Exploring the use of association rules in random forest for predicting heart disease

Abstract

Heart disease is one of the most dangerous diseases in the world. People with these diseases, most of them end up losing their lives. Therefore, machine learning algorithms have proven to be useful in this sense to help decision-making and prediction from the large amount of data generated by the healthcare sector. In this work, we have proposed a novel method that allows increasing the performance of the classical random forest technique so that this technique can be used for the prediction of heart disease with its better performance. We used in this study other classifiers such as classical random forest, support vector machine, decision tree, Naïve Bayes, and XGBoost. This work was done in the heart dataset Cleveland. According to the experimental results, the accuracy of the proposed model is better than that of other classifiers with 83.5%.This study contributed to the optimization of the random forest technique as well as gave solid knowledge of the formation of this technique.

Keywords:

• Heart disease
• association rules
• decision tree
• random forest
• Naïve Bayes
• support vector machine
• XGBoost

Disclosure statement

The authors did not disclose any possible conflicts of interest.

Additional information

Funding

The author(s) reported there is no funding associated with the work featured in this article.