Abstract
Heart diseases are one of the main causes of death around the world. The most reliable method for heart disease diagnosis is angiography, which is costly, invasive and has the risk of death. This study applies variations of decision tree (DT), support vector machine (SVM) and voting algorithms to construct a heart disease diagnosis predictive model. We show that integrating medical knowledge and statistical knowledge as well as fine tuning the parameters of the used models lead to more effective heart disease diagnosis models. We use two methods for implementing the proposed model. The obtained results in both methods show that voting algorithm and random forest outperform other methods. Moreover, the achieved accuracies show improvements over other existing methods.
Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callMore From: International Journal of Medical Engineering and Informatics
Disclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
