A hybrid approach to medical decision-making: diagnosis of heart disease with machine-learning model

Tamilarasi Suresh, Tsehay Admassu Assegie, Subhashni Rajkumar, Napa Komal Kumar

Abstract


Heart disease is one of the most widely spreading and deadliest diseases across the world. In this study, we have proposed hybrid model for heart disease prediction by employing random forest and support vector machine. With random forest, iterative feature elimination is carried out to select heart disease features that improves predictive outcome of support vector machine for heart disease prediction. Experiment is conducted on the proposed model using test set and the experimental result evidently appears to prove that the performance of the proposed hybrid model is better as compared to an individual random forest and support vector machine. Overall, we have developed more accurate and computationally efficient model for heart disease prediction with accuracy of 98.3%. Moreover, experiment is conducted to analyze the effect of regularization parameter (C) and gamma on the performance of support vector machine. The experimental result evidently reveals that support vector machine is very sensitive to C and gamma.

Keywords


heart disease; hybrid approach; medical decision making; random forest; support vector machine;

Full Text:

PDF


DOI: http://doi.org/10.11591/ijece.v12i2.pp1831-1838

Creative Commons License
This work is licensed under a Creative Commons Attribution-ShareAlike 4.0 International License.

International Journal of Electrical and Computer Engineering (IJECE)
p-ISSN 2088-8708, e-ISSN 2722-2578